SYSTEM OF THE COMPLICATED PRACTICAL PROBLEMS ANALYSIS

The original methodology of the system analysis of the inhomogeneous problem is offered, including stages of its reducing to homogeneous parts and selecting for them appropriate toolkits: methods and models. This system applies the accumulated knowledge and the experts skills to refer of each homogeneous problem to one or several alternative classes of modelling methods: analytical methods, statistical methods, artificial neuronets, knowledge based systems, fuzzy systems, genetic algorithms. The knowledge base testing has shown sufficiency and consistency of knowledge for realization of the inhomogeneous problems analysis even in conditions with a low and average distortion in the problem descriptions.

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A Proposal of Knowledge-Based Systems Using Fuzzy Rules and Genetic Algorithms

Journal of Japan Society for Fuzzy Theory and Systems ◽

10.3156/jfuzzy.8.6_76 ◽

1996 ◽

Vol 8 (6) ◽

pp. 1058-1065

Author(s):

Andreas BASTIAN ◽

Isao HAYASHI

Keyword(s):

Genetic Algorithms ◽

Fuzzy Rules ◽

Knowledge Based Systems ◽

Knowledge Based

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Intelligent Computation for Manufacturing

Research Methods ◽

10.4018/978-1-4666-7456-1.ch076 ◽

2015 ◽

pp. 1748-1782

Author(s):

Ashraf Afify

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Fuzzy Logic ◽

Inductive Learning ◽

Knowledge Based Systems ◽

Computation Techniques ◽

Practical Applications ◽

Knowledge Based

Intelligent computation refers to intelligence artificially realised through computation. This chapter reviews six intelligent computation techniques. They are: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, and swarm intelligent techniques. All of these tools have found many practical applications. Examples of applications in manufacturing are given in the chapter.

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Computational Intelligence

Manufacturing Intelligence for Industrial Engineering - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-60566-864-2.ch005 ◽

2010 ◽

pp. 111-136 ◽

Cited By ~ 1

Author(s):

Zude Zhou ◽

Huaiqing Wang ◽

Ping Lou

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Computational Intelligence ◽

Analytical Methods ◽

Fuzzy Systems ◽

Academic Community ◽

Neural Computing ◽

New Paradigm ◽

Combined System ◽

Robust System

In the 1990s, a new paradigm of science characterized by uncertainty, nonlinearity, and irreversibility and tackling complex problems was generally recognized by the academic community. In this new paradigm, traditional analytical methods are ineffectual, and there is recognition of the need to explore new methods to solve the more flexible, more robust system problems. In 1994 the first Computational Intelligence Conference in Orlando, Florida, US, first combined three different areas, smart neural networks, fuzzy systems and genetic algorithms, not only because the three have many similarities, but also because a properly combined system of the three is more effective than a system generated by one single technical field. Various theories and approaches of computational intelligence including neural computing, fuzzy computing and evolutional computing are comprehensively introduced in this chapter.

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