On the Application of Various Probabilistic Neural Networks in Solving Different Pattern Classification Problems
S. Ramakrishnan · 2008
A Probabilistic Neural Network (PNN) is defined as an implementation of statistical algorithm called Kernel discriminate analysis in which the operations are organized into multilayered feed forward network with four layers: input layer, pattern layer, summation layer and output layer. A PNN is predominantly a classifier since it can map any input pattern to a number of classifications. Among the main advantages that discriminate PNN is: Fast training process, an inherently parallel structure, guaranteed to converge to an optimal classifier as the size of the representative training set increases and training samples can be added or removed without extensive retraining. Accordingly, a PNN learns more quickly than many neural networks model and have had success on a variety of applications. Based on these facts and advantages, PNN can be viewed as a supervised neural network that is capable of using it in system classification and pattern recognition. The main objective of this paper is to describe the possible use of various PNN in solving some problems arising in signal processing and pattern recognition. The main attention is devoted to application of PNN in various classification problems like: classification brain tissues in multiple sclerosis, classification image texture, classification of soil texture and EEG pattern classification. Experimental results have been carried out and it verify the ability of modified PNN in achieving good classification rate in compared with traditional PNN or back propagation neural network BPNN and KNN.