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Foundations of fuzzy neural computations
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IJCAI '91 Proceedings of the Workshops on Fuzzy Logic and Fuzzy Control
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Fuzzy Neural Network Theory and Application
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Computational Optimization and Applications
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
Solving Fuzzy Linear Regression with Hybrid Optimization
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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Computers in Industry
Advances in Fuzzy Systems - Special issue on High Performance Fuzzy Systems for Real World Problems
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Fuzzy artificial neural networks (FANNs), which are the generalizations of artificial neural networks (ANNs), refer to connectionist systems in which all inputs, outputs, weights and biases may be fuzzy values. This paper proposes a two-phase learning method for FANNs, which reduces the generated error based on genetic algorithms (GAs). The optimization process is held on the alpha cuts of each fuzzy weight. Global optimized values of the alpha cuts at zero and one levels are obtained in the first phase and optimal values of several other alpha cuts are obtained in the second phase. Proposed method is shown to be superior in terms of generated error and executed time when compared with basic GA-based algorithms.