Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which...

Synchronization is very important for the efficient processing and transmission of information across the nervous system. Synchronization processes are ubiquitous in nature and play a very important role in many areas. As a special neural networks, fuzzy neural networks have an automatic adaption procedure and attract many attentions to their dynamical behaviors. In this paper, delayed fuzzy hi...

Fuzzy techniques have been originally designed to describe imprecise (“fuzzy”) expert knowledge. Somewhat surprisingly, fuzzy techniques have also been successfully used in situations without expert knowledge, when all we have is data. In this paper, we explain this surprising phenomenon by showing that the need for optimal processing of data (including crisp data) naturally leads to fuzzy and ...

Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy logic may become universal approximators for adaptive systems. Similar considerations can be extended to neural networks.

In this paper, by employing a new Lyapunov functional and an elementary inequality analysis technique, some sufficient conditions are derived to ensure the existence and uniqueness of periodic oscillatory solution for fuzzy bi-directional memory (BAM) neural networks with time-varying delays, and all other solutions of the fuzzy BAM neural networks converge the uniqueness periodic solution. The...

The goal of this work is to compare fuzzy, neural network and neuro-fuzzy approaches to the control of mobile robots. The rst part of this paper is devoted to the formal framework of fuzzy controllers. Results of an example of their use for a mobile robot are discussed. As an experimental platform, the Khepera mobile robot is used. The same example is studied using artiicial neural networks. Fo...

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a f...

Fuzzy associative memories belong to the class of fuzzy neural networks that employ fuzzy operators such as fuzzy conjunctions, disjunctions, and implications in order to store associations of fuzzy patterns. Fuzzy associative memories are generally used to implement fuzzy rule-based systems. Applications of FAMs include backing up a truck and trailer, target tracking, human-machine interfaces,...

This paper gives a short introduction into Fuzzy Set Theory, presents an overview on fuzzy controllers, and discusses possible combinations between fuzzy controllers and neural networks. Fuzzy Sets suggested by L.A. Zadeh 32] ooer a possibility to formally describe linguistic expressions like tall, fast, medium, etc., and to operate on them. Fuzzy controllers use fuzzy sets to represent linguis...