RECURRENT NEURAL NETWORKS AND SOFT COMPUTING
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....
