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 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 fuzzy
• Analyzes the Stability of Learning Algorithms for FNN
• Contains algorithms that are applicable to real time environments, notably for Unmanned Vehicle control
• Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy systems
• Provides MatLab code for some of the algorithms in the book so that development time for possible modifications is greatly reduced