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 these 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.4) Based on Learning: Many FLCs have been built to emulate human decision- making behavior, but few are focused on human learning, that is, the ability to create fuzzy logic rules and to modify them based on experience. Although ... schemes have been proposed around 1975 based on the concept of the aquot; linguistic phase planeaquot; [158, 159], they were heuristic, manual, and based on existing rule bases.
|Title||:||Neural Fuzzy Control Systems with Structure and Parameter Learning|
|Author||:||C. T. Lin, Ching Tai Lin|
|Publisher||:||World Scientific - 1994-01-01|