Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

Conjugate Gradient Related Publications

2 Signature Recognition Using Conjugate Gradient Neural Networks

Authors: Jamal Fathi Abu Hasna

Abstract:

There are two common methodologies to verify signatures: the functional approach and the parametric approach. This paper presents a new approach for dynamic handwritten signature verification (HSV) using the Neural Network with verification by the Conjugate Gradient Neural Network (NN). It is yet another avenue in the approach to HSV that is found to produce excellent results when compared with other methods of dynamic. Experimental results show the system is insensitive to the order of base-classifiers and gets a high verification ratio.

Keywords: Segmentation, Matlab software, Signature Verification, Conjugate Gradient, Skilled Forgery, and Genuine

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1 An Improved Conjugate Gradient Based Learning Algorithm for Back Propagation Neural Networks

Authors: N. M. Nawi, M. R. Ransing, R. S. Ransing

Abstract:

The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.

Keywords: back-propagation, activation function, search direction, Conjugate Gradient, Adaptive gain variation

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