Computational Intelligence : Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing.

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Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Siddique, Nazmul
Otros Autores: Adeli, Hojjat
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : Wiley, 2013.
Temas:
Computational intelligence.
Intelligence informatique.
Computational intelligence
Acceso en línea:Texto completo
Tabla de Contenidos:
  • COMPUTATIONAL INTELLIGENCE: SYNERGIES OF FUZZY LOGIC, NEURAL NETWORKS AND EVOLUTIONARY COMPUTING; Contents; Foreword; Preface; Acknowledgements; 1 Introduction to Computational Intelligence; 1.1 Computational Intelligence; 1.2 Paradigms of Computational Intelligence; 1.3 Approaches to Computational Intelligence; 1.3.1 Fuzzy Logic; 1.3.2 Neural Networks; 1.3.3 Evolutionary Computing; 1.3.4 Learning Theory; 1.3.5 Probabilistic Methods; 1.3.6 Swarm Intelligence; 1.4 Synergies of Computational Intelligence Techniques; 1.5 Applications of Computational Intelligence.
  • 1.6 Grand Challenges of Computational Intelligence1.7 Overview of the Book; 1.8 MATLAB® Basics; References; 2 Introduction to Fuzzy Logic; 2.1 Introduction; 2.2 Fuzzy Logic; 2.3 Fuzzy Sets; 2.4 Membership Functions; 2.4.1 Triangular MF; 2.4.2 Trapezoidal MF; 2.4.3 Gaussian MF; 2.4.4 Bell-shaped MF; 2.4.5 Sigmoidal MF; 2.5 Features of MFs; 2.5.1 Support; 2.5.2 Core; 2.5.3 Fuzzy Singleton; 2.5.4 Crossover Point; 2.6 Operations on Fuzzy Sets; 2.7 Linguistic Variables; 2.7.1 Features of Linguistic Variables; 2.8 Linguistic Hedges; 2.9 Fuzzy Relations; 2.9.1 Compositional Rule of Inference.
  • 2.10 Fuzzy If-Then Rules2.10.1 Rule Forms; 2.10.2 Compound Rules; 2.10.3 Aggregation of Rules; 2.11 Fuzzification; 2.12 Defuzzification; 2.13 Inference Mechanism; 2.13.1 Mamdani Fuzzy Inference; 2.13.2 Sugeno Fuzzy Inference; 2.13.3 Tsukamoto Fuzzy Inference; 2.14 Worked Examples; 2.15 MATLAB® Programs; References; 3 Fuzzy Systems and Applications; 3.1 Introduction; 3.2 Fuzzy System; 3.3 Fuzzy Modelling; 3.3.1 Structure Identification; 3.3.2 Parameter Identification; 3.3.3 Construction of Parameterized Membership Functions; 3.4 Fuzzy Control; 3.4.1 Fuzzification; 3.4.2 Inference Mechanism.
  • 3.4.3 Rule Base3.4.4 Defuzzification; 3.5 Design of Fuzzy Controller; 3.5.1 Input/Output Selection; 3.5.2 Choice of Membership Functions; 3.5.3 Creation of Rule Base; 3.5.4 Types of Fuzzy Controller; 3.6 Modular Fuzzy Controller; 3.7 MATLAB® Programs; References; 4 Neural Networks; 4.1 Introduction; 4.2 Artificial Neuron Model; 4.3 Activation Functions; 4.4 Network Architecture; 4.4.1 Feedforward Networks; 4.5 Learning in Neural Networks; 4.5.1 Supervised Learning; 4.5.2 Unsupervised Learning; 4.6 Recurrent Neural Networks; 4.6.1 Elman Networks; 4.6.2 Jordan Networks; 4.6.3 Hopfield Networks.
  • 4.7 MATLAB® ProgramsReferences; 5 Neural Systems and Applications; 5.1 Introduction; 5.2 System Identification and Control; 5.2.1 System Description; 5.2.2 System Identification; 5.2.3 System Control; 5.3 Neural Networks for Control; 5.3.1 System Identification for Control Design; 5.3.2 Neural Networks for Control Design; 5.4 MATLAB® Programs; References; 6 Evolutionary Computing; 6.1 Introduction; 6.2 Evolutionary Computing; 6.3 Terminologies of Evolutionary Computing; 6.3.1 Chromosome Representation; 6.3.2 Encoding Schemes; 6.3.3 Population; 6.3.4 Evaluation (or Fitness) Functions.

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