Cerebellar Model Articulation Controller

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.

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.

Forecasting cash demands at automatic teller machines (ATMs) is challenging, due to the heteroskedastic nature of such time series. Conventional global learning computational intelligence (CI) models, with their generalized learning behaviors, may not capture the complex dynamics and time-varying characteristics of such real-life time series data efficiently. In this paper, we propose to use a novel local learning model of the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative memory network to produce accurate forecasts of ATM cash demands. As a computational model of the human cerebellum, our model can incorporate local learning to effectively model the complex dynamics of heteroskedastic time series. We evaluated the forecasting performance of our PSECMAC model against the performances of current established CI and regression models using the NN5 competition dataset of 111 empirical daily ATM cash withdrawal series. The evaluation results show that the forecasting capability of our PSECMAC model exceeds that of the benchmark local and global-learning based models. c

Liver performs several numbers of metabolic functions that are essential to human life. These functions make the liver one of the most important organs in the human body. There are diseases that occur in the liver in short time (acute) and long time (chronic). These diseases could occur because of medications, alcohol, viruses, or excessive fat accumulation or deposit in the liver. Some of these diseases are the inflammation of the liver, insufficient liver performance, Hepatitis A, B, C, D, and liver cirrhosis. If the liver malfunctions in anyway, people know that they are putting their life at risk. For this reason, diagnosing any disease in the liver is important and sometimes difficult. It is also important to notice the diagnosis of the patient at an early stage as the symptoms arise so that the patient might be able to carry on a normal life. The objective of this article is to diagnose the liver disease using an application of the CMAC (Cerebellar Model Articulation Controlle...

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.

This paper presents a system based on a higher order Cerebeilar Model Articulation Controller to improve the linearity and to compensate the hysteresis of a control valve. To highlight the benefits and drawbacks of the proposed approach, a Masoneilan Camflex 11 valve is used as a practical test bead. These results show that CMAC can effectively improve the valve performance. This improvement can be obtained by first collecting a set of data and then allowing CMAC to learn the necessary actions to compensate the non-linear effects.

This work presents the development of a Proportional Integral Derivative (PID) digital controller with gain planning based on (CMAC) Cerebellar Model Articulation Controller. This digital controller operates in closed loop and has a dynamic gain adjustment. The control strategy uses an algorithm that calculates the proportional, integral and derivative parcels. This algorithm provides the soft start requirements. Both, the gain planning and the soft start requirements, uses auxiliary variables to determine an appropriate and dynamic setup of the digital controller. These auxiliary variables impose several restrictions on the digital controller and this one must have adaptive characteristics. The artificial neural network is used to estimate these auxiliary variables. The learning process depends on the data acquisition or the mathematical model simulations. The digital controller can operate in real-time conditions and the sample frequency can reach 10kHz.