Abstract
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 discretization 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.
This paper is supported by the National Natural Science Foundation of China under Grant No. 60175024 and the Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education of China.
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Huang, YX., Zhou, CG., Zou, SX., Wang, Y., Liang, YC. (2004). A Rough-Set-Based Fuzzy-Neural-Network System for Taste Signal Identification. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_53
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DOI: https://doi.org/10.1007/978-3-540-28648-6_53
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