Academia.eduAcademia.edu

Outline

Title
Abstract
Conclusion
References
All Topics
Computer Science
Artificial Intelligence

FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC

Profile image of Chai QuekChai Quek

2006, Ieee Transactions on Neural Networks a Publication of the Ieee Neural Networks Council

https://doi.org/10.1109/TNN.2006.880362
visibility

…

description

17 pages

link

1 file

Abstract

The cerebellum is a brain region important for a number of motor and cognitive functions. It is able to generate error correction signals to drive learning and for the acquisition of memory skills. The cerebellar model articulation controller (CMAC) is a neural network inspired by the neurophysiologic theory of the cerebellum and is recognized for its localized generalization and rapid algorithmic computation capabilities. The main deficiencies in the basic CMAC structure are: 1) it is difficult to interpret the internal operations of the CMAC network and 2) the resolution (quantization) problem arising from the partitioning of the input training space. These limitations lead to the synthesis of a fuzzy quantization technique and the mapping of a fuzzy inference scheme onto the CMAC structure. The discrete incremental clustering (DIC) technique is employed to alleviate the quantization problem in the CMAC structure, resulting in the fuzzy CMAC (FCMAC) network. The Yager inference scheme (Yager), which possesses firm fuzzy logic foundation and maps closely to the logical implication operations in the classical (binary) logic framework, is subsequently mapped onto the FCMAC structure. This results in a novel fuzzy neural architecture known as the fuzzy cerebellar model articulation controller-Yager (FCMAC-Yager) system. The proposed FCMAC-Yager network exhibits learning and memory capabilities of the cerebellum through the CMAC structure while emulating the human way of reasoning through the Yager. The new FCMAC-Yager network employs a two-phase training algorithm consisting of structural learning based on the DIC technique and parameter learning using hebbian learning (associative long-term potentiation). The proposed FCMAC-Yager architecture is evaluated using an extensive suite of real-life applications such as highway traffic-trend modeling and prediction and performing as an early warning system for bank failure classification and medical diagnosis of breast cancer. The experimental results are encouraging.

Related papers

The Kernel Addition Training Algorithm: Faster Training for CMAC Based Neural Networks
David newth

2001

The rapidly increasing size of databases creates a need for new algorithms to solve multi-class categorisation problems. Machine learning techniques such as neural networks have been successfully applied to this class of problems. However training times for these techniques can blow out as the size of the database increases. Some of the desirable features of algorithms for large databases are low order time complexity, training with only a single pass of the data, and accountability for class assignment decisions. We propose a new training algorithm for Cerebellar Model Articulation Controller (CMAC) based classifiers, which possesses these features. The training algorithm proposed here is based on a kernel addition method. An empirical investigation of this training method has found it to be superior to traditional techniques both in accuracy and time required to learn mappings between input vectors and class labels.

View PDFchevron_right
GEMM-eMFIS (FRI/E): A Novel General Episodic Memory Mechanism For Fuzzy Neural Networks
Sheng Wei Pang

2020 International Joint Conference on Neural Networks (IJCNN)

In fields such as finance, medicine, engineering, and science, making real-time predictions during transient periods characterized by sudden and large changes is a hard challenge for machine learning. Humans keep memory of these transient events, abstractly learn the most relevant rules and reuse them when similar events occur, which stems from episodic memory that allows storage and recall of similar events. This paper proposes a novel online general episodic memory mechanism (GEMM) and demonstrates its integration into the Neuro-Fuzzy system (NFS) architecture called evolving Mamdani Fuzzy Inference System (eMFIS) with Fuzzy Rule Interpolation and Extrapolation (FRI/E). Our proposition, called GEMM-eMFIS(FRI/E), learns from past events by storing and retrieving them from an episodic memory cache during event-driven transient behavior, thereby boosting performance while using a few rules only. GEMM-eMFIS(FRI/E) further has several in-built mechanisms that enable it to learn effectively from continuous stream of online data. They include associative-dissociative learning theory to keep its rule base updated, 2-stage incremental clustering; (2-SIC) to determine cluster width, interpolation and extrapolation of rules to deal with concept shifts and drifts in the time-variant data, and rule pruning and merging to keep the rule base compact. GEMM-eMFIS (FRI/E) is benchmarked against other NFS' on various time-variant datasets such as stock index prices and rainfall runoff with 3%-5% improvement during transient period and shows strong forecasting performances with 4%-5% more interpretability with lesser rules.

View PDFchevron_right
A neural fuzzy system with fuzzy supervised learning
Chin-Teng Lin

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996

A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use a-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fumy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.

View PDFchevron_right
Neuro-fuzzy architectures based on complex fuzzy logic
Sara Aghakhani

2010

View PDFchevron_right
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Suresh Sundaram

Applied Soft Computing, 2012

In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy inference system, referred to as, 'Meta-Cognitive Neuro-Fuzzy Inference System' (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi-Sugeno-Kang type-0 neuro-fuzzy inference system. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms.

View PDFchevron_right
High-order MS CMAC neural network
Shih-lin Hung

IEEE Transactions on Neural Networks, 2001

A macro structure cerebellar model articulation controller (CMAC) or MS_CMAC was developed by connecting several one-dimensional (1-D) CMACs as a tree structure, which decomposes a multidimensional problem into a set of 1-D subproblems, to reduce the computational complexity in multidimensional CMAC. Additionally, a trapezium scheme is proposed to assist MS_CMAC to model nonlinear systems. However, this trapezium scheme cannot perform a real smooth interpolation, and its working parameters are obtained through cross-validation. A quadratic splines scheme is developed herein to replace the trapezium scheme in MS_CMAC, named high-order MS_CMAC (HMS_CMAC). The quadratic splines scheme systematically transforms the stepwise weight contents of CMACs in MS_CMAC into smooth weight contents to perform the smooth outputs. Test results affirm that the HMS_CMAC has acceptable generalization in continuous function-mapping problems for nonoverlapping association in training instances. Nonoverlapping association in training instances not only significantly reduces the number of training instances needed, but also requires only one learning cycle in the learning stage. Index Terms-Cerebellar model articulation controller (CMAC), high-order MS_CMAC, macro structure cerebellar model articulation controller (MS_CMAC), quadratic splines.

View PDFchevron_right
PSECMAC: A Novel Self-Organizing Multiresolution Associative Memory Architecture
Edmund M Lai

IEEE Transactions on Neural Networks, 2000

The cerebellum constitutes a vital part of the human brain system that possesses the capability to model highly nonlinear physical dynamics. The CMAC associative memory network is a computational model inspired by the neurophysiological properties of the cerebellum, and it has been widely used for control, optimization and various pattern recognition tasks. However, the CMAC network's highly regularized computing structure often leads to: (1) a suboptimal modeling accuracy; (2) poor memory utilization; and (3) the generalization-accuracy dilemma. Previous attempts to address these shortcomings have limited success and the proposed solutions often introduce a high operational complexity to the CMAC network. This paper presents a novel neurophysiologically-inspired associative memory architecture named PSECMAC that non-uniformly allocates its computing cells to overcome the architectural deficiencies encountered by the CMAC network. The non-uniform memory allocation scheme employed by the proposed PSECMAC network is inspired by the cerebellar experience-driven synaptic plasticity phenomenon observed in the cerebellum, where significantly higher densities of synaptic connections are located in the frequently-accessed regions. In the PSECMAC network, this biological synaptic plasticity phenomenon is emulated by employing a data-driven adaptive memory quantization scheme that defines its computing structure. A neighborhood based activation process is subsequently implemented to facilitate the learning and computation of the PSECMAC structure. The training stability of the PSECMAC network is theoretically assured by the proof of its learning convergence which will be presented in this paper. The performance of the proposed network is subsequently benchmarked against Manuscript received xxxxx; revised xxxxx; accepted xxxxx S. D. Teddy is with the Data Mining Department, Institute for Infocomm Research, Singapore, 119613. The work in this paper was performed during her PhD candidature at 2 the CMAC network and several representative CMAC variants on three real-life applications, namely:

View PDFchevron_right
Fuzzy Associative Conjuncted Maps Network
Chai Quek

IEEE Transactions on Neural Networks, 2000

The fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that associates data of nonlinearly related inputs and outputs. In the network, each input or output dimension is represented by a feature map that is partitioned into fuzzy or crisp sets. These fuzzy sets are then conjuncted to form antecedents and consequences, which are subsequently associated to form IF-THEN rules. The associative memory is encoded through an offline batch mode learning process consisting of three consecutive phases. The initial unsupervised membership function initialization phase takes inspiration from the organization of sensory maps in our brains by allocating membership functions based on uniform information density. Next, supervised Hebbian learning encodes synaptic weights between input and output nodes. Finally, a supervised error reduction phase fine-tunes the network, which allows for the discovery of the varying levels of influence of each input dimension across an output feature space in the encoded memory. In the series of experiments, we show that each phase in the learning process contributes significantly to the final accuracy of prediction. Further experiments using both toy problems and real-world data demonstrate significant superiority in terms of accuracy of nonlinear estimation when benchmarked against other prominent architectures and exhibit the network's suitability to perform analysis and prediction on real-world applications, such as traffic density prediction as shown in this paper.

View PDFchevron_right
Modeling of fuzzy-neural systems using the coevolutionary algorithm
Samir Omanovic

Proceedings of the 12th Wseas International Conference on Neural Networks Fuzzy Systems Evolutionary Computing Automation, 2011

This paper presents a novel hybridization of the fuzzy logic, the neural network and the coevolutionary algorithm for building a fuzzy-neural system (or a Mamdani fuzzy system) from data. The novel hybridization uses the coevolution of many species, and proposes the coevolution of groups of similar species, both for the optimization of the structure of the fuzzy-neural network. In the fuzzy-neural network the coevolution changes the number of nodes and their parameters, which indirectly change the number of fuzzy sets and their parameters and the number of rules and their parameters. Specific backpropagation that supports the Mamdani type of fuzzy system is proposed for small size optimization of fuzzy sets parameters. The backpropagation is active while the average absolute error is small, otherwise the backpropagation stops and the coevolution is active. To be able to guide the coevolution based on three criteria the coevolution uses three level of the fitness. It is possible to control the overfitting through these criteria. The proposed hybridization and its Matlab implementation can be used for creating Mamdani fuzzy-neural systems or simply Mamdani fuzzy systems, from data. This is an alternative for ANFIS and similar hybridizations. It offers to users possibility of building a Mamdani fuzzy-neuro system from data, automatically, with optimizing the number of rules, controlling the overfitting, working with large data sets and many variables, using simple triangular fuzzy sets, the result with high function approximation and good knowledge presentation, the result that can be used as the Mamdani fuzzy system (Matlab FIS object) or as the neural network (internal presentation format and feedforward function). This paper also presents the results of testing with the Wisconsin Breast Cancer Database, from UCI Machine Learning Repository ([

View PDFchevron_right
CMAC neural networks structures
mohammad seifi

2009

Cerebellar Model Articulation Controller (CMAC) NN is a computational model of cerebellum introduced as an alternative to backpropagated multilayer networks to control robot arms. From then it has seen many improvements and has been applied in many other areas as a general NN. These improvements have been in the context of generalization, learning techniques, differentiability, memory size, fuzzification and hardware implementation. This paper is a systematic review of CMAC's different structures and applications.

View PDFchevron_right

References (75)

  1. J. S. Albus, "A theory of cerebellar function," Math Biosci., vol. 10, pp. 25-61, 1971.
  2. --, "Theoretical and experimental aspects of a cerebellar model," Ph.D. dissertation, Univ. Maryland, College Park, 1972.
  3. --, "A new approach to manipulator control: the cerebellar model articulation controller (CMAC)," J. Dyn. Syst., Meas. Control, Trans. ASME, vol. 97, no. 3, pp. 220-227, 1975.
  4. K. K. Ang, C. Quek, and A. Wahab, "MCMAC-CVT: A novel on-line associative memory based CVT transmission control system," Neural Netw., vol. 15, no. 2, pp. 219-236, 2001.
  5. --, "POPFNN-CRI(S): Pseudo outer product-based fuzzy neural network using the compositional rule of inference and singleton fuzzi- fier," IEEE Trans. Syst., Man, Cybern. B, vol. 33, no. 6, pp. 838-849, Dec. 2003.
  6. K. K. Ang and C. Quek, "RSPOP: Rough set-based pseudo outer- product fuzzy rule identification algorithm," Neural Comput., vol. 17, no. 1, pp. 205-243, 2005.
  7. --, "Stock trading using RSPOP: A novel rough set-based neuro-fuzzy approach," IEEE Trans. Neural Netw., vol. 17, no. 5, pp. 1301-1315, Sep. 2006.
  8. K. P. Bennet and O. L. Mangasarian, "Neural network programming via linear programming," in Advances in Optimisation and Parallel Computing, P. M. Parlados, Ed. Amsterdam, The Netherlands: El- sevier, 1992, pp. 56-57.
  9. J. C. Bezdek, R. J. Hathaway, and W. T. Tucker, "An improved conver- gence theory for the fuzzy c-means clustering algorithms," in Analysis of Fuzzy Information 3. Boca Raton, FL: CRC Press, 1987, ch. 8, pp. 123-129.
  10. The Breast Cancer Site [Online]. Available: http://www.thebreast- cancersite.com/cgi-bin/WebObjects/CTDSites
  11. J. J. Buckley and Y. Hayashi, "Neural nets for fuzzy systems," Fuzzy Sets Syst., vol. 71, pp. 265-276, 1995.
  12. J. Calle, Breast Cancer Facts and Figures. Washington, DC: Amer- ican Cancer Society, 2003-2004, pp. 1-27.
  13. G. A. Carpenter, S. Grossberg, and D. B. Rosen, "Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system," Neural Netw., vol. 4, pp. 759-771, 1991.
  14. P. Y. K. Cheng, "Predicting bank failures: A comparison of the Cox proportional hazards model and the time varying covariates model," Ph.D. dissertation, Nanyang Business School, Nanyang Technological Univ., Singapore, 2002.
  15. Federal Reserve Bank of Chicago, Repository for bank data [Online]. Available: http://www.chicagofed.org
  16. R. Cole and J. Gunther, "Separating the likelihood and timing of bank failure," J. Banking Finance, vol. 19, no. 6, pp. 1073-1089, 1995.
  17. M. A. Gluck and C. E. Myers, Gateway to Memory-An Introduction to Neural Network Modeling of the Hippocampus and Learning. Cam- bridge, MA: MIT Press, 2000.
  18. R. N. Goldman and J. S. Weinberg, Statistics: An Introduction. En- glewood Cliffs, NJ: Prentice-Hall, 1985.
  19. J. A. Gomez-Ruiz et al., "A combined neural network and decision trees model for prognosis of breast cancer relapse," Artif. Intell. Med., vol. 27, pp. 45-63, 2003.
  20. Z. Y. Guo, FCMAC-AARS: Approximate analogical reasoning based fuzzy CMAC School of Computer Engineering, Nanyang Technolog- ical Univ., Singapore, Final Year Project Rep., 2003.
  21. R. F. Harrison, J. Downs, and S. S. Cross, "A decision support tool for the diagnosis of breast cancer based upon fuzzy ARTMAP," Neural Comput. Applicat., vol. 7, pp. 147-165, 1998.
  22. D. O. Hebb, The Organization of Behaviour: A Neuropsychological Theory. New York: Wiley, 1949.
  23. H. Jiang, Fuzzy CMAC implementing CRI School of Computer Engi- neering, Nanyang Technological Univ., Singapore, Final Year Project Rep., 2003.
  24. C. C. Jou, "A fuzzy cerebellar model articulation controller," in Proc. 1992 IEEE Int. Conf. Fuzzy Syst., San Diego, CA, 1992, pp. 1171-1178.
  25. E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Principles of Neural Science, 4th ed. New York: McGraw-Hill, 2000, ch. 42&62.
  26. J. Ker, C. Hsu, Y. Kuo, and B. Liu, "A fuzzy CMAC model for color reproduction," Fuzzy Sets Syst., vol. 91, no. 1, pp. 53-68, 1997.
  27. B. G. Kermani, M. W. White, and H. T. Nagle, "Feature extraction for genetic algorithms in neural networks for breast cancer classification," in Proc. 1995 IEEE Eng. Med. Biol., 1995, pp. 831-832.
  28. D. Kim, "A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation," Fuzzy Sets Syst., vol. 125, no. 1, pp. 93-104, 2002.
  29. T. K. Kohonen, "Self-organized formation of topologically correct fea- ture maps," Biol. Cybern., vol. 43, pp. 59-69, 1982.
  30. W. Lane, S. Looney, and J. Wansley, "An application of the Cox pro- portional hazards model to bank failure," J. Banking Finance, vol. 10, pp. 511-531, 1986.
  31. S. H. Lane, D. A. Handelman, and J. J. Gelfand, "Theory and devel- opment of higher-order CMAC neural networks," IEEE Control Syst. Mag., vol. 12, no. 2, pp. 23-30, Apr. 1992.
  32. C. T. Lin, Neural Fuzzy Control Systems With Structure and Parameter Learning. Singapore: World Scientific, 1994.
  33. C. T. Lin and C. S. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Syn- ergism to Intelligent Systems. Englewood Cliffs, NJ: Prentice-Hall, 1996.
  34. H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Boston, MA: Kluwer, 1998.
  35. E. H. Mamdani, "Application of fuzzy logic to approximate reasoning using linguistic systems," IEEE Trans. Comput., vol. C-26, no. 12, pp. 1182-1191, Dec. 1977.
  36. D. Marr, "A theory of cerebellar cortex," J. Physiol., vol. 202, pp. 437-470, 1969.
  37. --, "Early warning of bank failure: a logit regression approach," J. Banking Finance, vol. 1, no. 3, pp. 249-276, 1977.
  38. J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduc- tion and New Directions. Upper Saddle River, NJ: Prentice-Hall, 2001.
  39. W. T. Miller, F. H. Glanz, and L. G. Kraft, "CMAC: An associative neural network alternative to backpropagation," Proc. IEEE, vol. 78, no. 10, pp. 1561-1567, Oct. 1990.
  40. J. H. Nie and D. A. Linkens, "A fuzzified CMAC self-learning con- troller," in Proc. 2nd IEEE Int. Conf. Fuzzy Syst., 1993, vol. 1, pp. 500-505.
  41. R. J. Oentaryo, Auto-driving using GenSoYager School of Computer Engineering, Nanyang Technological Univ., Singapore, Final Year Project Rep., 2004.
  42. J. Ozawa, I. Hayashi, and N. Wakami, "Formulation of CMAC-fuzzy system," in Proc. IEEE Int. Conf. Fuzzy Syst., 1992, pp. 1179-1186.
  43. M. Pasquier, C. Quek, and M. Toh, "Fuzzylot: A novel self-organizing fuzzy-neural rule-based pilot system for automated vehicles," Neural Netw., vol. 14, no. 8, pp. 1099-1112, 2001.
  44. K. H. Quah and C. Quek, MCES: A Novel Monte Carlo Evaluative Se- lection Approach for Objective Feature Selections in Data Modeling. Singapore: School of Computer Engineering, Nanyang Technological Univ., to be published.
  45. K. H. Quah and C. Quek, "FITSK: online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation," IEEE Trans. Syst., Man, Cybern. B, vol. 36, no. 1, pp. 166-178, Feb. 2006.
  46. --, "Maximum reward reinforcement learning: A non-cumulative reward criterion," Expert Syst. Applicat., vol. 31, no. 2, pp. 351-359, 2006.
  47. C. Quek and R. W. Zhou, "POPFNN: A pseudo outer-product based fuzzy neural network," Neural Netw., vol. 9, no. 9, pp. 1569-1581, 1996.
  48. --, "POPFNN-AARS: a pseudo outer-product based fuzzy neural network," IEEE Trans. Syst., Man, Cybern., vol. 29, no. 6, pp. 859-870, Dec. 1999.
  49. C. Quek and R. W. Zhou, "The POP learning algorithms: Reducing work in identifying fuzzy rules," Neural Netw., vol. 14, no. 10, pp. 1431-1445, 2001.
  50. C. Quek, B. Tan, and V. Sagar, "POPFNN-based fingerprint verifica- tion system," Neural Netw., vol. 14, pp. 305-323, 2001.
  51. C. Quek and R. W. Zhou, "Antiforgery: A novel pseudo-outer product based fuzzy neural network driven signature verification system," Pat- tern Recognit. Lett., vol. 23, no. 14, pp. 1795-1816, 2002.
  52. C. Quek and A. Singh, "POP-Yager: A novel self-organising fuzzy neural network based on the Yager inference," Expert Syst. Applicat., vol. 29, no. 1, pp. 229-242, 2005.
  53. C. Quek and R. W. Zhou, "Structure and learning algorithms of a non- singleton input fuzzy neural network based on the approximate ana- logical reasoning schema," Fuzzy Sets Syst., vol. 157, pp. 1814-1831, 2006.
  54. R. Sentiono, "Extracting rules from pruned neural networks for breast cancer diagnosis," Artif. Intell. Med., vol. 8, no. 1, pp. 37-51, 1996.
  55. S. J. Shema et al., "Socio-economic status and breast carcinoma sur- vival in four racial/ethnic groups: A population-based study," Amer. Cancer Soc., pp. 1303-1311, 2003.
  56. J. E. W. Sim, "FCMAC-Yager: Yager inference scheme based fuzzy CMAC," Final Year Project Thesis, School of Computer Engineering, Nanyang Technological Univ., Singapore, 2004.
  57. J. Sinkey, Jr., "A multivariate statistical analysis of the characteristics of problem banks," J. Finance, vol. 1, pp. 21-36, 1975.
  58. W. N. Street, O. L. Mangasarian, and W. H. Wolberg, "Pattern recogni- tion via linear programming: Theory and application," Med. Diagnosis, pp. 22-31, 1990.
  59. G. K. Tan, "Feasibility of predicting congestion states with neural net- works," Final Year Project Thesis, School of Civil Structural Engi- neering, Nanyang Technological Univ., Singapore, 1997.
  60. T. Z. Tan, C. Quek, and G. S. Ng, "Ovarian cancer diagnosis by hip- pocampus and neocortex-inspired learning memory structure," Neural Netw., vol. 18, no. 5-6, pp. 818-825, 2005.
  61. T. Z. Tan, C. Quek, and G. S. Ng, "Biological brain-inspired genetic complementary learning for stock market and bank failure prediction," Comp. Intell., 2006, to be published.
  62. S. D. Teddy, E. M.-K. Lai, and C. Quek, "FQCMAC: a multi-resolute cerebellar learning memory model," in Proc. 3rd Int. Conf. Comp. In- tell., Robot. Auton. Syst. (CIRAS), 2005.
  63. S. D. Teddy, C. Quek, and E. M.-K. Lai, "PSECMAC: A brain-inspired multi-resolution cerebellar learning memory model," IEEE Trans. Neural Netw., 2006, submitted for publication.
  64. C. W. Ting, "Fuzzy associative memory architecture," First year report of Ph.D., School of Computer Engineering, Nanyang Technological Univ., Singapore, 2002.
  65. W. L. Tung and C. Quek, "GenSoFNN: A generic self-organising fuzzy neural network," IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1075-1086, Sep. 2001.
  66. --, "DIC: a novel discrete incremental clustering technique for the derivation of fuzzy membership functions," in Proc. 7th Pacific Rim Int. Conf. Artif. Intell., Tokyo, Japan, 2002, pp. 178-187.
  67. --, "Falcon: Fuzzy neural control and decision systems using FKP and PFKP clustering algorithms," IEEE Trans. Syst., Man, Cybern. B, vol. 34, no. 1, pp. 686-694, Feb. 2004.
  68. W. L. Tung, C. Quek, and P. Cheng, "GenSo-EWS: A novel neural- fuzzy based early warning system for predicting bank failures," Neural Netw., vol. 17, no. 4, pp. 567-587, 2004.
  69. W. L. Tung and C. Quek, "GenSo-FDSS: A neural-fuzzy decision sup- port system for pediatric ALL cancer subtype identification using gene expression data," Artif. Intell. Med., vol. 33, no. 1, pp. 61-88, 2005.
  70. Wisconsin Diagnostic Breast Cancer Database [Online]. Available: htttp://www.cs.wisc.edu/~olvi/uwmp/cancer.html
  71. R. R. Yager, J. M. Keller, and H. Tahani, "Neural network implemen- tation of fuzzy logic," Fuzzy Sets Syst., vol. 45, no. 1, pp. 1-12, 1992.
  72. L. A. Zadeh, "Fuzzy logic, neural networks and soft computing," Commun. ACM, vol. 37, no. 3, pp. 77-84, 1994.
  73. K. Zhang and F. Qian, "Fuzzy CMAC and its application," in Proc. 3rd World Congr. Intell. Control Automat., 2000, pp. 944-947.
  74. J. Sim received the B.Eng. degree in computer en- gineering from Nanyang Technological University, Singapore, in 2004. She is currently working towards the M.S. degree in financial mathematics from the University of Western Australia, Australia.
  75. W. L. Tung received the B.A.Sc. (first-class honors) and Ph.D. degrees in computer engineering from Nanyang Technological University (NTU), Singa- pore, in 2000 and 2004, respectively.

Related papers

Parametric CMAC networks: Fundamentals and applications of a fast convergence neural structure
Paulo E M Almeida, Marcelo Simoes

IEEE Transactions on Industry Applications, 2003

This paper shows fundamentals and applications of the parametric cerebellar model arithmetic computer (P-CMAC) network: a neural structure derived from the Albus CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. It resembles the original CMAC proposed by Albus in the sense that it is a local network, (i.e., for a given input vector, only a few of the networks nodes-or neurons-will be active and will effectively contribute to the corresponding network output). The internal mapping structure is built in such a way that it implements, for each CMAC memory location, one linear parametric equation of the network input strengths. This mapping can be corresponded to a hidden layer in a multilayer perceptron (MLP) structure. The output of the active equations are then weighted and averaged to generate the actual outputs to the network. A practical comparison between the proposed network and other structures is, thus, accomplished. P-CMAC, MLP, and CMAC networks are applied to approximate a nonlinear function. Results show advantages of the proposed algorithm based on the computational efforts needed by each network to perform nonlinear function approximation. Also, P-CMAC is used to solve a practical problem at mobile telephony, approximating an RF mapping at a given region to help operational people while maintaining service quality.

View PDFchevron_right
GenSoFNN-Yager: A novel brain-inspired generic self-organizing neuro-fuzzy system realizing Yager inference
Richard Oentaryo

Expert Systems With Applications, 2008

Pattern recognition is increasingly becoming a key component of decision support systems (DSSs) in many application areas, especially when automatically extracting semantic rules from data is a chief concern. Accordingly, this paper presents a novel evolving neuro-fuzzy DSS, the generic self-organizing fuzzy neural network realizing Yager inference (GenSoFNN-Yager), that emulates the sequential learning paradigm of the hippocampus in the brain to synthesize from low-level numerical data to high-level declarative fuzzy rules. The proposed system exhibits simple and conceptually firm computational steps that correspond closely to a plausible human logical reasoning and decision-making. Experimental results on sample benchmark problems and realistic medical diagnosis applications show the potential of the proposed system as a competent DSS.

View PDFchevron_right
Building Practical Classifiers Using Cerebellar Model Associative Memory Neural Networks
David Cornforth

Proc. Conf Artificial Neural Networks and Expert …, 2001

How can cerebellar model neural networks be successfully applied to automated classification? Simple modifications and a new training scheme are applied to the Cerebellar Model Articulation Controller (CMAC). This results in a classifier with fast training time, guaranteed convergence and a small number of parameters. How can model parameters be set to achieve optimum classifier accuracy, without lengthy empirical trials? A consideration of the most significant sources of classification error results in a simple method for estimating the optimum range for the parameters. The method is tested using empirical trials, and shown to be reliable. This makes the modified CMAC a desirable choice for automated classification in the context of pattern recognition and data mining.

View PDFchevron_right
Traffic Prediction Using Ying-Yang Fuzzy Cerebellar Model Articulation Controller
Chai Quek

18th International Conference on Pattern Recognition (ICPR'06), 2006

Traffic prediction is a critical element in traffic control today. With the increase of transportation, an effective traffic prediction allows to prevent traffic problems. This research aims to propose a novel approach to traffic prediction using Ying-Yang Fuzzy Cerebellar Model Articulation Controller (YY-FCMAC). The model is motivated from the famous Chinese ancient Ying-Yang philosophy, which views everything as a product of conflict-harmony process between Ying and Yang. That principle is applied to find the optimal number of clusters and fuzzy sets in the fuzzification phase of the hybrid fuzzy-neural YY-FCMAC network. The analyzed experiment on a set of real traffic data flow of the eastbound Pan Island Expressway (PIE) in Singapore shows the effectiveness of the YY-FCMAC in universal approximation and prediction.

View PDFchevron_right
Two Novel Learning Algorithms for CMAC Neural Network Based on Changeable Learning Rate
Nazal Modhej

Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon or slower convergence speed due to larger fixed or smaller fixed learning rate respectively. The present research deals with offering two solutions for this problem. The original idea of the present research is using changeable learning rate at each state of training phase in the CMAC model. The first algorithm deals with a new learning rate based on reviation of learning rate. The second algorithm deals with number of training iteration and performance learning, with respect to this fact that error is compatible with inverse training time. Simulation results show that this algorithms have faster convergence and better performance in comparison to conventional CMAC model in all training cycles .

View PDFchevron_right

Related topics

  • Algorithms
  • Artificial Intelligence
  • Fuzzy Inference
  • Bank failure
  • Breast Cancer Diagnosis
  • Cerebellar Model Articulation Co...
  • Generalization Error

    • Cited by

    A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty
    Whye Tung

    Neural Computing and Applications, 2007

    Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert's knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a braininspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging.

    View PDFchevron_right
    A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading
    Chai Quek

    International Journal of Fuzzy System Applications, 2011

    In this paper, a novel stock trading framework based on a neuro-fuzzy associative memory (FAM) architecture is proposed. The architecture incorporates the approximate analogical reasoning schema (AARS) to resolve the problem of discontinuous (staircase) response and inefficient memory utilization with uniform quantization in the associative memory structure. The resultant structure is conceptually clearer and more computationally efficient than the Compositional Rule Inference (CRI) and Truth Value Restriction (TVR) fuzzy inference schemes. The local generalization characteristic of the associative memory structure is preserved by the FAM-AARS architecture. The prediction and trading framework exploits the price percentage oscillator (PPO) for input preprocessing and trading decision making. Numerical experiments conducted on real-life stock data confirm the validity of the design and the performance of the proposed architecture.

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved