Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning

Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning

Kayacan, Erdal
Ahmadieh, Mojtaba

73,79 €(IVA inc.)

Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning provides readers with a comprehensive understanding of a process that is increasingly being applied to control and identification areas. As sample applications like unmanned vehicles need to provide fast feedback to input data, and algorithms for machine learning related to real time applications must be fast and offer the lowest computational burden possible to embedded systems, this book provides the basics of FNN (Type-1 and Type-2) as well as the models and algorithms that best address the needs of real time systems. A thorough discussion on stability analysis of algorithms is provided as is MatLab code for example applications. Users will find a valuable source for researchers coming into this exciting area who lack a previous background in artificial intelligence. Presents hybrid training methods for type-1 and type-2 fuzzyAnalyzes the Stability of Learning Algorithms for FNNContains algorithms that are applicable to real time environments, notably for Unmanned Vehicle controlIntroduces fast and simple adaptation rules for type-1 and type-2 fuzzy systemsProvides MatLab code for some of the algorithms in the book so that development time for possible modifications is greatly reduced INDICE: Chapter 1: Preliminary mathematics Chapter 2: Fundamentals of type-1 fuzzy logic theory Chapter 3: Fundamentals of type-2 fuzzy logic theory Chapter 4: Fundamentals of artificial neural networks Chapter 5: Type-1 fuzzy neural networks Chapter 6: Type-2 fuzzy neural networks Chapter 7: Parameter adaptation rules of FNNs using GD based training algorithms Chapter 8: Parameter adaptation rules of FNNs using EKF based training algorithms Chapter 9: Parameter adaptation rules of FNNs using SMC Chapter 10: Hybrid parameter adaptation rules of FNNs by using PSO Chapter 11: Identification examples

  • ISBN: 978-0-12-802687-8
  • Editorial: Butterworth-Heinemann
  • Encuadernacion: Rústica
  • Páginas: 320
  • Fecha Publicación: 01/11/2015
  • Nº Volúmenes: 1
  • Idioma: Inglés