Hybrid Intelligent Engineering Systems

The following sections are included:

• PREFACE

• CONTENTS

This chapter presents a general introduction to the intelligent system's techniques such as knowledge-based systems, artificial neural network, fuzzy logic systems and genetic algorithms. A number of problems can be solved using these techniques. The combined use of these techniques is also discussed.

This chapter describes four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to automatic design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to automatic design fuzzy systems, and (4) a fuzzy knowledgebased system to control genetic parameters dynamically.

Hybrid intelligent systems are becoming a part of the repertoire of software systems developers for industrial applications of artificial intelligence. The integration of neural networks and expert systems has proven to be a useful way to develop real-world applications, including the areas of control systems, robotics, diagnostic systems, and industrial operations. This chapter emphasizes the integration of neural networks and expert systems, models for hybrid systems, and the roles of fuzzy logic, genetic algorithms, and case-based reasoning in neuro-expert systems. An emerging opportunity for integrating intelligent systems is the use as intelligent agents of distributed systems such as LAN's and the world-wide web.

Researchers at the Universities of Alabama and South Australia have developed a technique that utilizes the search capabilities of genetic algorithms to enhance the process control capabilities of fuzzy logic. Genetic algorithms are search algorithms based on the mechanics of natural genetics. Fuzzy logic is a process that affords computers the capability of manipulating abstract concepts commonly used by humans in decision making. Together, genetic algorithms and fuzzy logic possess the qualities needed in adaptive control systems. This chapter describes a process by which genetic algorithms can be used to develop efficient fuzzy controllers.

Researchers at the Universities of Alabama and South Australia have developed a technique that utilizes the search capabilities of genetic algorithms to enhance the process control capabilities of fuzzy logic. This chapter describes several applications of the technique.

This chapter reports on progress made in the first 3 years of ATR's “CAM-Brain” Project, which aims to use “evolutionary engineering” techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MIT's Cellular Automata Machine “CAM-8” or NTT's Content Addressable Memory System “CAM-CAM”. The states of a billion (later a trillion) 3D cellular automata cells, and millions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MIT's “CAM-8” (essentially a serial device) can update 200 million CA cells a second. It is likely that NTT's “CAM-CAM” (Cellular Automata on Content Addressable Memory) is essentially a massively parallel device, and will be able to update a hundred billion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry, namely “brain building”. Building artificial brains with a billion neurons is the aim of ATR's 8 year “CAM-Brain” research project, ending in 2001.

Recent advances in the theory and applications of artificial neural networks also called neural networks, fuzzy systems, genetic algorithms (Gas) and chaos theory have generated tremendous interest in fusion technology. This chapter presents the fusion technology of neural network and chaos, fuzzy system and chaos, genetic algorithms and chaos and the applications of chaos theory.