Neural Fuzzy Control Systems With Structure And Parameter Learning

Name: Neural Fuzzy Control Systems With Structure And Parameter Learning
Brand: World Scientific Publishing Co Pte Ltd
SKU: 9789810216139
Price: 22893 HUF
Availability: InStock

Szerző: Chin-Teng Lin

Nyelv:

Kötés: Kemény kötésű

Kiadó: World Scientific Publishing Co Pte Ltd

Elérhetőség: 50 % esély

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A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed i...

Információk a könyvről

Szerző

Chin-Teng Lin

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

1994

oldal

144

EAN

9789810216139

ISBN

9810216130

Enbook ID

05064625

Kiadó

World Scientific Publishing Co Pte Ltd

Súly

318

Teljes leírás

A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms, a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm. Both of the above two learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.

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Neural Fuzzy Control Systems With Structure And Parameter Learning

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