Knowledge learning on fuzzy expert neural networks
Hsin-Chia Fu, Jean Jyh-Jiun Shann, Hsiao-Tien Pao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The proposed fuzzy expert network is an event-driven, acyclic neural network designed for knowledge learning on a fuzzy expert system. Initially, the network is constructed according to a primitive (rough) expert rules including the input and output linguistic variables and values of the system. For each inference rule, it corresponds to an inference network, which contains five types of nodes: Input, Membership-Function, AND, OR, and Defuzzification Nodes. We propose a two-phase learning procedure for the inference network. The first phase is the competitive backpropagation (CBP) training phase, and the second phase is the rule- pruning phase. The CBP learning algorithm in the training phase enables the network to learn the fuzzy rules as precisely as backpropagation-type learning algorithms and yet as quickly as competitive-type learning algorithms. After the CBP training, the rule-pruning process is performed to delete redundant weight connections for simple network structures and yet compatible retrieving performance.