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RECURRENT NEURAL NETWORKS AND SOFT COMPUTING

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RECURRENT NEURAL NETWORKS AND SOFT COMPUTING

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This section illustrates some general concepts of artificial neural networks, their properties, mode of training, static training (feedforward) and dynamic training (recurrent), training data classification, supervised, semi-supervised and unsupervised training. Prof. Belic Igor’s chapter that deals with ANN application in modeling, illustrating two properties of ANN: universality and optimization. Prof. Shoukry Amin discusses both symbolic and non-symbolic data and ways of bridging neural networks with fuzzy logic, as discussed in the second chapter including an application to the robot problem. Dr. Hajjari Tayebeh discusses fuzzy logic and various ordering indices approaches in detail, including defuzzification method, reference set method and the fuzzy relation method....

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