NEURAL NETWORKS

NEURAL NETWORKS

ABSTRACT:

Artificial neural networks are a method of computation and information processing that takes advantage of today's technology. Mimmicing the processes found in biological nuerons

l artificial neural networks are used to predict and learn from a given set a data. Neural networks are more robust at data analysis than statistical methods because of their ability to handle small variations of parameters and noise.The basic element of a neural network is the perceptron. First proposed by Frank Rosenblatt in 1958 at Cornell University, the perceptron has 5 basic elements:an n-vector input, weights, summing function, threshold device, and an output. Outputs are in the form of -1 and/or +1. The threshold has a setting which governs the output based on the summation of input vectors. If the summation falls below the threshold setting, a -1 is the output. If the summation exceeds the threshold setting, +1 is the output. A more technical investigation of a Single Neuron Preceptron shows that it can have an input vector X of N dimensions. These inputs go through a vector W of Weights of N dimension. Processed by the Summation Node, "a" is generated where "a" is the "dot product" of vectors X and W plus a Bias. "A" is then processed through an activation function which compares the value of "a" to a predefined Threshold. If "a" is below the Threshold, the perceptron will not fire. If it is above the Threshold, the perceptron will fire one pulse whose amplitude is predefined.


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