Fundamental Simulation Methodologies Dwelling in Neural Networks

Neural networks are processing devices which are either algorithms or actual hard wares. Their designs are motivated by the design and functioning of human brains and components thereof. Neural networks provide improved performance over conventional technologies in the areas of machine vision, robust pattern detection, signal filtering, virtual reality, data segmentation, data compression, data mining, text mining, artificial life, adaptive control, optimization and scheduling, complex mapping and many more. In this book the fundamental simulation methodologies of the neural networks - McCulloch Pitts neuron model, Hebbs network, perceptron network, ADALINE neuron model, MADALINE neurons model, hetero associative memory network, auto associative memory network, bidirectional associative memory network, discrete Hopfield network, back propagation network, self organizing map network, learning vector quantization network, max net, mexican hat network, hamming net and counter propagation network are described and illustrated with the help of algorithms, MATLAB source codes and outputs.