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1995 | Buch

Overview of Intelligent Systems Kapitel lesen Erstes Kapitel lesen

Hybrid Intelligent Systems

verfasst von: Larry R. Medsker

Verlag: Springer US

Enthalten in: Professional Book Archive

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Über dieses Buch

Hybrid Intelligent Systems summarizes the strengths and weaknesses of five intelligent technologies: fuzzy logic, genetic algorithms, case-based reasoning, neural networks and expert systems, reviewing the status and significance of research into their integration. Engineering and scientific examples and case studies are used to illustrate principles and application development techniques. The reader will gain a clear idea of the current status of hybrid intelligent systems and discover how to choose and develop appropriate applications. The book is based on a thorough literature search of recent publications on research and development in hybrid intelligent systems; the resulting 50-page reference section of the book is invaluable.
The book starts with a summary of the five major intelligent technologies and of the issues in and current status of research into them. Each subsequent chapter presents a detailed discussion of a different combination of intelligent technologies, along with examples and case studies. Four chapters contain detailed case studies of working hybrid systems. The book enables the reader to: Describe the important concepts, strengths and limitations of each technology; Recognize and analyze potential problems with the application of hybrid systems; Choose appropriate hybrid intelligent solutions; Understand how applications are designed with any of the approaches covered; Choose appropriate commercial development shells or tools. An invaluable reference source for those who wish to apply intelligent systems techniques to their own problems.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Overview of Intelligent Systems
Abstract
Several intelligent computing technologies are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. This chapter presents the fundamentals of individual intelligent technologies that will be important for understanding their integration.
Larry R. Medsker
Chapter 2. Research in Hybrid Intelligent Systems
Abstract
The emerging need for hybrid intelligent systems is currently motivating important research and development work. The individual technologies represent the various aspects of human intelligence that are necessary for enhancing decision making in computing systems. In addition to the practical aspect, systems that perform a variety of intelligent tasks are interesting also for expanding the artificial intelligence effort to gain a better understanding of human cognition.
Larry R. Medsker
Chapter 3. Expert Systems and Neural Networks
Abstract
Expert system and neural network technologies have developed to the point that the advantages of each can be combined into more powerful systems. In some cases, neural computing systems are replacing expert systems and other artificial intelligence solutions. In other applications, neural networks provide features not possible with conventional AI systems and may provide aspects of intelligent behavior that have thus far eluded AI.
Larry R. Medsker
Chapter 4. Industrial Experience: The use of Hybrid Systems in the Power Industry
Abstract
The electricity supply industry in the United Kingdom has undergone continuous change, ever since its beginnings in the late 19th century. From the early days when the industry consisted of numerous small companies, largely meeting local needs for lighting, industry and public transport, the industry has evolved to become one of major importance to the country’s economy. Until recently, the industry accounted for the some three-quarters of the UK coal market, about a third of the country’s primary fuel of the UK coal market, and about a third of the country’s primary fuel consumption. Revenues from the industry amount to almost 2% of the national income. The price and security of the electricity supply is a key factor in the competitiveness of UK industry.
John Maclntyre, Peter Smith, Tom Harris
Chapter 5. Expert Networks: Theory and Applications
Abstract
Philosophically, the study of expert networks stems from a desire to capitalize on the major strengths of both expert systems and neural networks. The major thrust of this type of hybrid system is to synthesize the capability of expert systems to capture expert domain knowledge in an inference-based system with the power of black-box neural networks trained from example data.
Susan I. Hruska, Tarina A. Whitfield
Chapter 6. Fuzzy Logic and Expert Systems
Abstract
The combination of fuzzy logic and expert systems is a fundamental technique flowing directly from the nature of fuzzy logic. Fuzzy expert systems are currently the most popular use of fuzzy logic with many applications now operational in a diverse range of subjects.
Larry R. Medsker
Chapter 7. Fuzzy Systems and Neural Networks
Abstract
Research and development in the use of fuzzy systems with neural networks has been proceeding at a rapid pace during the last few years and applications are starting to be developed. A natural integration follows the same successful path of hybrid neural network and expert systems by using fuzzy rule based systems instead of traditional expert systems. Thus all the various ways of integration are available for fuzzy connectionist systems. Additionally, neural networks can be used as tools for designing and tuning fuzzy systems. And, fuzzy principles can be used in the design of neural networks, embedding fuzziness in the internal workings of the basically neural system.
Larry R. Medsker
Chapter 8. Genetic Algorithms and Neural Networks
Abstract
The integration of genetic algorithms with neural networks is a rapidly expanding area building on the explosion of interest in the two technologies individually. In the early 1990’s, the revolution in the research and application of neural networks was followed by a surge in activity for genetic algorithms. By 1994, several conferences and publications had been dedicated to genetic algorithms where they had formerly been an adjunct of neural network or expert systems forums.
Larry R. Medsker
Chapter 9. Applications Using Hybrid Neural Networks with Fuzzy Logic and Genetic Algorithms
Abstract
The preceding chapters covered the concepts and principles of the integration of neural networks with fuzzy logic and with genetic algorithms. This chapter presents three case studies of applications of these types of hybrid systems. They are examples of many projects carried out at The University of Tennessee and Oak Ridge National Laboratory by groups headed by Professor Robert E.Uhrig.
Robert E. Uhrig, Anna Loskiewicz-Buczak, Zhicaho Guo
Chapter 10. Genetic Algorithms and Fuzzy Systems
Abstract
The integration of genetic algorithms with fuzzy systems is newer and less well explored than the combining of genetic algorithms or fuzzy logic with neural networks. The pioneering work in this area starts around 1989 and much of the initiative is due to Charles Karr. The most promising application area for the short term is the use of genetic algorithms to improve fuzzy logic controllers. This is an emerging field in which important work has proven the usefulness in some areas, but further studies will explore additional creative ways of integration.
Larry R. Medsker
Chapter 11. Adaptive Control of an Exothermic Chemical Reaction System Using Fuzzy Logic and Genetic Algorithms
Abstract
Establishing suitable control of complex chemical reactions, a requirement in a number of industries, poses a difficult problem because of nonlinearities and frequently changing process dynamics. Researchers at the University of Alabama and the U. S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLCs) that are capable of effectively managing such complex chemical systems. In this technique, a genetic algorithm (GA) is used to alter the membership functions employed by a conventional FLC, an approach that is contrary to the stratagem traditionally used to provide FLCs with adaptive capabilities in which the rule set is altered. The current approach is used to produce an adaptive GA-FLC for a particular system in which an exothermic chemical reaction is conducted. Specifically, formaldehyde is reacted with ammonia in a continuous stirred tank reactor to produce hexamine and water. Results indicate that FLCs augmented with GAs offer a powerful alternative to conventional process control techniques in the nonlinear, rapidly changing chemical systems commonly found in industry.
Charles L. Karr
Chapter 12. Genetic Algorithms and Expert Systems
Abstract
While expert systems and conventional operations research techniques are effective for a wide range of problems, other tasks and complex real-world problems are impossible or at least difficult to address with these technologies. For example, applications involving scheduling and resource planning continue to be challenging subjects of research and development. Generic methods are sought that can be applied easily to practical problems. Genetic algorithms are receiving considerable attention and are proving to be important in practice. However, the addition of domain knowledge through heuristic rales can have a positive effect on the performance of genetic algorithm solutions.
Larry R. Medsker
Chapter 13. Hybrid Systems with Case-Based Reasoning
Abstract
Case-based reasoning is starting to be commercially attractive and an important alternative for knowledge-based system designers, developers, integrators, and tool vendors as a way to leverage the valuable experience within organizations [Mott, 1993]. The potential market is significant, especially for decision making in complex problem-solving domains, as well as for efficient information processing. Most of the initial work combined case-based reasoning with rule-based systems, but other hybrid combinations are starting to be considered. Other areas that will likely see a boost in productivity from hybrid case-based reasoning systems include intelligent text retrieval, hypertext, data mining, and telecommunications Foster, 1992.
Larry R. Medsker
Chapter 14. Summary and the Future of Hybrid Intelligent Systems
Abstract
The integration of intelligent technologies has become an interesting research topic and a powerful way to develop useful applications. Most of the work has been done on conventional hardware, but future work will include implementation with massively parallel systems, optical computing, and other hardware as they are improved and become economical. Hybrid intelligent systems (see Figure 14.1) also rely on and interface with conventional software technologies such as database systems and object-oriented programming languages.
Larry R. Medsker
Backmatter
Metadaten
Titel
Hybrid Intelligent Systems
verfasst von
Larry R. Medsker
Copyright-Jahr
1995
Verlag
Springer US
Electronic ISBN
978-1-4615-2353-6
Print ISBN
978-1-4613-5998-2
DOI
https://doi.org/10.1007/978-1-4615-2353-6