A neuro-fuzzy systems for control applications

IEEE International Symposium on Industrial Electronics. Proceedings. ISIE'98 (Cat. No.98TH8357), 1998

Arti cial Neural Networks ANNs and Fuzzy Systems FS are high parallel structures that consist of a large number of elementary nonlinear units called neurons fully interconnected.

1997

Arti cial Neural Networks NNs are high parallel structures that consist of a large number of elementary non-linear units called neurons fully interconnected.

The techniques of artificial intelligence based in fuzzy logic and neural networks are frequently applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Generically, when they are used in a combined way, they are called Neuro-Fuzzy Systems. This term, however, is often used to assign a specific type of system that integrates both techniques. This type of system is characterised by a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several different implementations of neuro-fuzzy systems, where each author defined its own model. This article summarizes a general vision of the area describing the most known hybrid neuro-fuzzy techniques, its advantages and disadvantages.

IEEE Transactions on Industry Applications, 1999

This paper presents a neuro-fuzzy approach to the development of high-performance real-time intelligent and adaptive controllers for non-linear plants. Several paradigms derived from cognitive sciences are considered and analyzed in this work, such as Neural Networks, Fuzzy Inference Systems, Genetic Algorithms, etc. The di erent control strategies are also integrated with Finite State Automata, and the theory of Fuzzy State Automata is derived from that. The novelty of the proposed approach resides in the tight i n tegration of the control strategies and in the capability of allowing a hybrid design. Finally, three practical applications of the proposed hybrid approach are analyzed.

IFAC Proceedings Volumes, 2005

The neuro fuzzy system based on two independent structures is described, the first a neuro-observer system developed by use of dynamical neural networks, and the second as the control system based on fuzzy logic system. These structures are described by independent way and their properties are analyzed. Besides, the neuro-fuzzy system performance is proved by the application to the Bergman th blood Insulin-Glucose interaction model, the simulations show the neuro-fuzzy output as the insulin infusor output (insulin concentration), the glucose concentration estimated state is also described, as well as the inferential rules and the membership functions in the fuzzy.

2004

Due to the complexity of walking robots which has in general a great number of degrees of freedom, cognitive modelling controller such as Fuzzy Logic, Neural Networks...seems to be reasonable in the design of adaptive control of such robot. Fuzzy Logic Controller is more used because it lets you describe desired system behaviour with simple “if-then” relations. But it has a major limitation because in many applications, the designer has to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but we cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, we show an original method to design an adaptive Neuro-Fuzzy controller which consists in five steps that are: the initial design of an ANFIS contro...

1997

The natural development o f h ybrid techniques causes biases with their roots in di erent technologies, in this case either in fuzzy systems or in neural networks. The neuro-fuzzy research is discussed in this paper giving examples and emphasising the neural network perspective. Introduction of new fuzzy systems models and the development of new neural learning algorithms could be observed in the development of neuro-fuzzy research. The self-evolving character of those new neural algorithms capable of building the architecture of neuro-fuzzy systems from data, proves to be an useful tool for data analysis and knowledge fusion applications.

Integration of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. Neuro-fuzzy modelling can be regarded as a gray-box technique on the boundary between neural networks and qualitative fuzzy models. The tools for building neuro-fuzzy models are based on combinations of algorithms from the fields of neural networks, pattern recognition and regression analysis. In this paper, an overview of neuro-fuzzy modelling given.

The use of a proposed recurrent hybrid neural network to control of walking robot with four legs is investigated in this paper. A neural networks based control system is utilized to the control of four-legged walking robot. The control system consists of four proposed neural controllers, four standard PD controllers and four-legged planar walking robot. The proposed neural network (NN) is employed as an inverse controller of the robot. The NN has three layers, which are input, hybrid hidden and output layers. In addition to feedforward connections from the input layer to the hidden layer and from the hidden layer to the output layer, there is also feedback connection from the output layer to the hidden layer and from the hidden layer to itself. The reason to use hybrid layer is that robot’s dynamics consists of linear and non-linear parts. The results show that the proposed neural control system has superior performance to control trajectory of walking robot with payload.

Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. The fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic cont rol strategy. The ability of fuzzy logic to handle imprecise and inconsistent real - world problems has made it suitable for a wide variety of applications. The present paper is concerned with modeling and control of nonlinear systems using fuzzy and neuro - f uzzy techniques. Design of controllers using conventional methods for nonlinear systems is difficult due to absence of a system atic theory behind it. In such cases, an approach based on the use of neural network for identifying the requirements o f the cont roller and the system from the input output data have been shown to be attractive. But identification using a neuro - fuzzy approach will help in reducing the arbitrariness in the choice of the type pf membership functions a nd the ranges of variables in the universe of discourse. This paper presents two methods based on fuzzy logic for the control of nonlinear systems, one using PID like fuzzy control and another using a neuro - fuzzy approach.