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30 March 2000 Certain improvements in back propagation procedure for pattern identification
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In this paper certain simple procedures are presented for stabilization of a class of NN trained by Back Propagation algorithm with minimum number of failures. To quicken the network response with minimum number of oscillations, a slope parameter is utilized in the bipolar sigmoidal activation function and is appropriately chosen with the help of Lyapunov's stability theorem. Further a new weight update scheme is proposed for the backpropagation algorithm. The above procedures are applied and tested with XOR problem, Iris data and image data for the choices of slope parameter, learning rate and momentum factor; its performance in terms of local minima, learning speed are evaluated and compared with the performance of traditional BP algorithm.
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S. N. Sivanandam, M. Paulraj, and Mathiyazhagan Nithyanandam "Certain improvements in back propagation procedure for pattern identification", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000);

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