A Rough-Set-Based Fuzzy-Neural-Network System for Taste Signal Identification

摘要

A voting-mechanism-based fuzzy neural network model for identifying 11 kinds of mineral waters by its taste signals is proposed. In the model, A classification rule extracting algorithm based on discretiza-tion methods in rough sets is developed to extract fewer but robust classification rules, which are ease to be translated to fuzzy if-then rules to construct a fuzzy neural network system. Finally, the particle swarm optimization is adopted to refine network parameters. Experimental results show that the system is feasible and effective.