Computational Intelligence Systems and Applications

1. Front Matter

   Pages i-x

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2. Introduction

   • Marian B. Gorzałczany

   Pages 1-15

3. Elements of the theory of fuzzy sets

   • Marian B. Gorzałczany

   Pages 17-51

4. Essentials of artificial neural networks

   • Marian B. Gorzałczany

   Pages 53-84

5. Brief introduction to genetic algorithms

   • Marian B. Gorzałczany

   Pages 85-101

6. Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems

   • Marian B. Gorzałczany

   Pages 103-125

7. Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data

   • Marian B. Gorzałczany

   Pages 127-190

8. Rule-based neuro-fuzzy modelling of dynamic system and designing of controllers

   • Marian B. Gorzałczany

   Pages 191-229

9. Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support

   • Marian B. Gorzałczany

   Pages 231-287

10. Fuzzy neural network for system modelling and control

    • Marian B. Gorzałczany

    Pages 289-314

11. Fuzzy neural classifier

    • Marian B. Gorzałczany

    Pages 315-330

12. Back Matter

    Pages 331-364

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Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real­ world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge "buried" in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge "encoded" in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.

• Artificial Intelligence
• Computational Intelligence
• Fuzzy
• Intelligent Decision Support
• Knowledge Discover
• Neuro-Fuzzy Systems
• artificial neural network
• decision support system
• decision tree
• genetic algorithms
• inference engine
• intelligence
• knowledge
• modeling
• neural network

• Department of Electrical and Computer Engineering, Kielce University of Technology, Kielce, Poland

  Marian B. Gorzałczany

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