a Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan 80424, ROC, Taiwan
b Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan 40227, ROC, Taiwan
Abstract:
A method for modeling the learning of belief combination in evidential reasoning using a neural network is presented. A centralized network composed of multiple bidirectional associative memories (BAMs) sharing a single output array of neurons is proposed to process the uncertainty management of many pieces of evidence simultaneously. The convergence properties of the multi-BAM network are proved. The combination process of evidence is considered as a resonant process through the multi-BAM networks. Most important of all, a majority rule of decision making in presentation of multiple evidence is also found by the study of signal-noise-ratio (SNR) of the multi-BAM network. Some simulation examples are given. The result is coherent with the intuition of reasoning.