Seminar On Artificial Neural Network

SEMINAR ON ANN
Arti cial neural networks are, as their name indicates, computational networks which attempt to simulate, in a gross manner, the networks of nerve cell (neurons) of the biological (human or animal) central nervous system. This simulation is a gross cell-by-cell (neuron-by-neuron, element-by-element) simulation. It borrows from the neurophysiological knowledge of biological neurons and of networks of such biological neurons. It thus di ers from conventional (digital or analog) computing machines that serve to replace, enhance or speed-up human brain computation without regard to organization of the computing elements and of their networking.Still, we emphasize that the simulation a orded by neural networks is very gross.Why then should we view arti cial neural networks (denoted below as neural networks or ANNs) as more than an exercise in simulation? We must ask this question especially since, computationally (at least), a conventional digital computer can do everything that an arti cial neural network can do.The answer lies in two aspects of major importance. The neural network, by its simulating a biological neural network, is in fact a novel computer architecture and a novel algorithmization architecture relative to conventional computers.

It allows using very simple computational operations (additions, multiplication and fundamental logic elements) to solve complex, mathematically ill-de ned problems, nonlinear problems or stochastic problems. A conventional algorithm will employ complex sets of equations, and will apply to only a given problem and exactly to it. The ANN will be (a) computationally and algorithmically very simple and (b) it will have a self-organizing feature to allow it to hold for a wide range of problems.For example, if a house avoids an obstacle or if a mouse avoids a cat, it certainly solves no di erential equations on trajectories, nor does it employ complex pattern recognition algorithms. Its brain is very simple, yet it employs a few basic neuronal cells that fundamentally obey the structure of such cells in advanced animals and in man. The arti cial neural network's solution will also aim at such (most likely not the same) simplicity.

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