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Accuracy Enhancement in Offline Signature Verification with The Use of Associative Memory


Anu Rathi, Amrita Ticku, Niti Gupta


Vol. 14  No. 3  pp. 138-142


A probabilistic neural network (PNN) is a feed forward neural network, which was derived from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis. The feed forward neural network was the first and arguably simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. Basically there consists of four layers : input layer, pattern layer, summation layer and output layer. The Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy


Signature verification, Offline, Bi-objective optimization, Associative Memory Net, OpenMP