Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
The modified self-organizing fuzzy neural network model for adaptability evaluation
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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In this paper, we introduce new fuzzy-neural networks – Fuzzy Set – based Polynomial Neural Networks (FSPNN) with a new fuzzy set-based polynomial neuron (FSPN) whose fuzzy rules include the information granules obtained through Information Granulation. We investigate the proposed networks from two different aspects to improve the performance of the fuzzy-neural networks. First, We have developed genetic optimization using Genetic Algorithms to find the optimal structure for fuzzy-neural networks. Second, we have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules. The performance of genetically optimized FSPNN (gFSPNN) with fuzzy set-based neural neuron (FSPN) involving information granules is quantified through experimentation.