Neuro-Fuzzy Architectures and Hybrid Learning by Danuta RutkowskaNeuro-Fuzzy Architectures and Hybrid Learning by Danuta Rutkowska

Neuro-Fuzzy Architectures and Hybrid Learning

byDanuta Rutkowska

Paperback | October 21, 2010

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The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe­ matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ­ ence of the human mind as a role model is clearly visible in the methodolo­ gies which have emerged, mainly during the past two decades, for the con­ ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.
Title:Neuro-Fuzzy Architectures and Hybrid LearningFormat:PaperbackDimensions:288 pagesPublished:October 21, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:379082500X

ISBN - 13:9783790825008

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Table of Contents

1 Introduction.- 2 Description of Fuzzy Inference Systems.- 2.1 Fuzzy Sets.- 2.1.1 Basic Definitions.- 2.1.2 Operations on Fuzzy Sets.- 2.1.3 Fuzzy Relations.- 2.1.4 Operations on Fuzzy Relations.- 2.2 Approximxate Reasoning.- 2.2.1 Compositional Rule of Inference.- 2.2.2 Implications.- 2.2.3 Linguistic Variables.- 2.2.4 Calculus of Fuzzy Rules.- 2.2.5 Granulation and Fuzzy Graphs.- 2.2.6 Computing with Words.- 2.3 Fuzzy Systems.- 2.3.1 Rule-Based Fuzzy Logic Systems.- 2.3.2 The Mamdani and Logical Approaches to Fuzzy Inference.- 2.3.3 Fuzzy Systems Based on the Mamdani Approach.- 2.3.4 Fuzzy Systems Based on the Logical Approach.- 3 Neural Networks and Neuro-Fuzzy Systems.- 3.1 Neural Networks.- 3.1.1 Model of an Artificial Neuron.- 3.1.2 Multi-Layer Perceptron.- 3.1.3 Back-Propagation Learning Method.- 3.1.4 RBF Networks.- 3.1.5 Supervised and Unsupervised Learning.- 3.1.6 Competitive Learning.- 3.1.7 Hebbian Learning Rule.- 3.1.8 Kohonen's Self-Organizing Neural Network.- 3.1.9 Learning Vector Quantization.- 3.1.10 Other Types of Neural Networks.- 3.2 Fuzzy Neural Networks.- 3.3 Fuzzy Inference Neural Networks.- 4 Neuro-Fuzzy Architectures Based on the Mamdani Approach.- 4.1 Basic Architectures.- 4.2 General Form of the Architectures.- 4.3 Systems with Inference Based on Bounded Product.- 4.4 Simplified Architectures.- 4.5 Architectures Based on Other Defuzzification Methods.- 4.5.1 COS-Based Architectures.- 4.5.2 Neural Networks as Defuzzifiers.- 4.6 Architectures of Systems with Non-Singleton Fuzzifier.- 5 Neuro-Fuzzy Architectures Based on the Logical Approach.- 5.1 Mathematical Descriptions of Implication-Based Systems.- 5.2 NOCFS Architectures.- 5.3 OCFS Architectures.- 5.4 Performance Analysis.- 5.5 Computer Simulations.- 5.5.1 Function Approximation.- 5.5.2 Control Examples.- 5.5.3 Classification Problems.- 6 Hybrid Learning Methods.- 6.1 Gradient Learning Algorithms.- 6.1.1 Learning of Fuzzy Systems.- 6.1.2 Learning of Neuro-Fuzzy Systems.- 6.1.3 FLiNN - Architecture Based Learning.- 6.2 Genetic Algorithms.- 6.2.1 Basic Genetic Algorithm.- 6.2.2 Evolutionary Algorithms.- 6.3 Clustering Algorithms.- 6.3.1 Cluster Analysis.- 6.3.2 Fuzzy Clustering.- 6.4 Hybrid Learning.- 6.4.1 Combinations of Gradient Methods, GAs, and Clustering Algorithms.- 6.4.2 Hybrid Algorithms for Parameter Tuning.- 6.4.3 Rule Generation.- 6.5 Hybrid Learning Algorithms for Neuro-Fuzzy Systems.- 6.5.1 Examples of Hybrid Learning Neuro-Fuzzy Systems.- 6.5.2 Description of Two Hybrid Learning Algorithms for Rule Generation.- 6.5.3 Medical Diagnosis Applications.- 7 Intelligent Systems.- 7.1 Artificial and Computational Intelligence.- 7.2 Expert Systems.- 7.2.1 Classical Expert Systems.- 7.2.2 Fuzzy and Neural Expert Systems.- 7.3 Intelligent Computational Systems.- 7.4 Perception-Based Intelligent Systems.- 8 Summary.- List of Figures.- List of Tables.- References.