Dynamic Evolving Neuro Fuzzy Systems of Qualitative Process

1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report, 1996

1 This paper describes DANIELA a Neuro-Fuzzy system for control applications. The system is based on a custom neural device that can implement either Multi-Layer Perceptrons, Radial Basis Functions or Fuzzy paradigms. The system implements intelligent control algorithms mixing neuro-fuzzy paradigms with nite state automatas and is used to control a walking hexapod.

Engineering Applications of Artificial Intelligence, 1994

The purpose of this paper is to develop a neuro-fuzzy controller (NFC) for adaptive tracking in unknown nonlinear dynamic systems, and for on-line computation of inverse kinematic transformations of robot manipulators. The NFC is comprised of a conventional fuzzy logic controller in the feedback configuration and a recurrent neural network in the inverse mode (feedforward) configuration. It is envisaged that the integration of fuzzy logic and neural-network-based controllers will encompass the merits of both the techniques, and thus provide a robust controller. The fuzzy logic controller (FLC), based on fuzzy set theory, provides a means for converting a linguistic control strategy into control actions and thus offering a high level of computation. On the other hand, a recurrent neural network provides low-level computations and embodies salient features such as learning, fault-tolerance, parallelism and generalization. The NFC uses a minimum of fuzzy rules for a given task. The proposed control scheme is implemented for controlling a class of unknown nonlinear dynamic systems, and for computing the in verse kinematic transformations of a two-linked robot.

Computer Modeling and Intelligent Systems

In this paper discusses the use of neuro-fuzzy control systems as a tool for managing nondeterministic objects in real time. This paper discusses modern control tools structural models of a discrete quasi-invariant automated control system. In this paper the analysis of an automated control system is presented, which is based on the use of typical models of discrete automated control systems. According to the proposed solution in the automated control system in real time it is proposed to use a neuro-fuzzy control system as a function of the object and the system's transfer ratio. The neuro-fuzzy control system is based on the learning process of an artificial neural network (ANN), which allows to define the rules of fuzzy inference (FIS). The paper proposes the ANFIS model, which is implemented by using the fuzzy system Takagi T., Sugeno M., also is considered an algorithm based on seven fuzzy rules. In this paper is presented a technique for implementing a neuro-fuzzy control system for non-deterministic objects by using Matlab. The use of Matlab made possible to create a model of an adaptive neuro-fuzzy inference system. The paper describes the process of training a neural network, where a hybrid method is chosen, which is a combination of the least squares method and the method of decreasing the inverse gradient. The results of testing a neuro-fuzzy control system of a non-deterministic object are presented, which confirmed the possibility of using a neuro-fuzzy control model. It is constructed the structure and the result in the form of a control surface of the neuro-fuzzy ANFIS model. The results presented in this paper allow to conclude about the possibility of using a neuro-fuzzy control system for non-deterministic objects in real time.

Classical control theory is based on the mathematical models that describe the physical plant under consideration. The essence of fuzzy control is to build a model of human expert who is capable of controlling the plant without thinking in terms of mathematical model. The transformation of expert's knowledge in terms of control rules to fuzzy frame work has not been formalized and arbitrary choices concerning, for example, the shape of membership functions have to be made.

2015

An adaptive identifier for neuro-fuzzy control system nonlinear dynamic object operating in conditions of uncertainty intrinsic properties and the environment. The algorithms of structural and parametric identification in real time, which is a combination of an identification algorithm coefficients of linear equations and the theory of interactive adaptation method. The hybrid model based on neural networks and fuzzy models, improves the efficiency of solving the problem of managing complex dynamic objects under uncertainty.

Anais do 5. Congresso Brasileiro de Redes Neurais, 2016

A fuzzy neural network (FNN) architecture for generating fuzzy rule database from sparse knowledge about the system dynamic is proposed and its performance in solving the truck backer-upper nonlinear control problem is analyzed. Since the FNN maps fuzzy input to fuzzy outputs vectors deriving a knowledge kernel for a given specific problem, considering the actual experience of experts in the domain, it may becomes an important tool for handling fuzzy mapping in many practical applications. Further improvements to allow fast convergence of the FNN learning phase, controlling the learning and momentum parameters through an adaptive fuzzy logic controller (FLC), is described. Simulations results are included to confirm that this approach may be of great practical interest since the actual results are very encouraging.

IEEE Transactions on Neural Networks, 1998

Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GA’s). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of an FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.

2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014

This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.

2015

This paper presents details on the implementation of evolving Takagi-Sugeno-Kang (TSK) fuzzy models of a nonlinear process represented by the pendulum dynamics in the framework of the representative pendulum-crane systems. The pendulum angle is the output variable of the TSK fuzzy models that are obtained by online identification. The rule bases and the parameters of the TSK fuzzy models are continuously evolved by an online identification algorithm (OIA) that adds new rules with more summarization power and modifies the existing rules and parameters. The OIA is associated with an input selection algorithm that guides the modelling in terms of ranking the inputs according to their importance factors. Three TSK fuzzy models evolved by the OIA are exemplified. The performance of the new evolving TSK fuzzy models is illustrated by experimental results conducted on pendulum-crane laboratory equipment.

2000

Adaptive neuro-fuzzy inference systems exhibit both the numeric power of neural networks and verbal power of fuzzy inference systems. Particularly in control applications, the prime difficulty in selecting the parameters of such systems stems from the unavailability of the desired control inputs. In this paper, a novel approach is presented for the establishment of a sliding mode in the plant under control. The approach presented adjusts solely the parameters of the defuzzifier. At the adjustment stage, a dynamic adaptation law is proposed and it is proved that the particularly chosen form of the adaptation strategy creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode. In the simulations, the dynamic model of a 2-DOF direct drive robotic manipulator is used. It is observed that the method discussed is highly robust against the disturbances like varying payload conditions and noisy observations