Illustrating how fuzzy logic and neural networks can be integrated into a model reference control context for real-time control of multivariable systems, this book provides a unified architecture which accommodates several popular learning/reasoning paradigms, including counter propagation networks, radial basis functions and CMAC within a fuzzy context. The book introduces new fuzzy-neural controller structures, and demonstrates the feasibility of the proposed approach by showing applications. It is designed for graduate students of neural networks, intelligent control and fuzzy matters in departments of electrical engineering, computer science and maths.
Sprache
Verlagsort
Verlagsgruppe
Pearson Education Limited
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
references, appendices, index
Maße
Höhe: 235 mm
Breite: 178 mm
Gewicht
ISBN-13
978-0-13-337916-7 (9780133379167)
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Schweitzer Klassifikation
A Unified Approximate Reasoning Approach. Multivariable Blood-Pressure Control. Constructing Rule-Bases by Self-Learning. Neural-Network Based Approximate Reasoning. BNN Network-Based Fuzzy Controller with Self-Learning. A Hybrid Neural-Network Based Self-Organizing Controller. CPN Network-Based Fuzzy Controller. Fuzzified CMAC and RBF Network-Based Self-Learning Controllers.
