Showing posts from May, 2015

Competitive Learning

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