Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
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Pattern classification is a process to determine whether an input pattern is or is not a member of a particular class. It is problem in which similar patterns are grouped together the grouping are then defined as classes. Pattern classification is one type of pattern recognition which has a lot of applications including Finger print classification, handwritten character recognition, speaker recognition. Artificial Neural network (ANN) is machine learning model which are information processing systems, inspired by biological neural systems. ANN have a potential of massive computation, online adaptation and learning abilities. Neural network consists of many simple processing elements joined by weighted connection paths. A neural net produces an output signal in response to an input pattern; the output is determined by value of weights. This paper makes a comparative study of various neural networks for pattern classification. The neural networks discussed in this paper are Perceptron based, VoD based SOM and RBF network.