Reducing Error Signal in Multilayer Perceptron Neural Networks using MLP for Label Ranking

This paper describes a simple tactile probe for identifying error signal in Multilayer. In multilayer having the number of hidden layers error signal can be process as irrespective manner so difficult to find out the error signal. The multilayer perceptron having the number of hidden layers with one output layer. This networks are fully connected i.e. a neuron in any layer of this network is connected to all the nodes/neurons in the previous layer signal flow through the network progress in a forward direction from left to right and on a layer by layer. In this networks we can identify the two kinds of networks. First one is Function Signal-A function signal is an input signal that comes in at the Input end of the network. Second one is Error Signal- an error signal originates at an output neuron of the network and propagates backward i.e. layer by layer through the network. In this paper, we adapt a multilayer perceptron algorithm for label ranking. We focus on the adaptation of the Back-Propagation (BP) mechanism.