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Competitive Learning

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In the networks with competitive and corporative learning, it can be said that the neurons compete and cooperate ones with the others to give a specific task. Differing from the net of Hebbian learning basis, where many output nodes can be activated simultaneously, in the case of competitive learning only one can be activated at a time. In this method , the neurons of the output   layer compete among themselves to become active. This feature is highly suited to discover the statistical features of a set of input patterns. Three elements of competitive learning            A set of neurons are same except for the randomly distributed weights. There fore each neuron responds differently to a given input.        A limit is imposed on the strength of each neuron.          A mechanism that permits the neuron to compete so that only one is active at a time. The neuron that wins is called winner-take-all-neuron. In its simplest form the network has a single layer of output