A Modified Backpropagation Training Algorithm for Feedforward Neural Networks
Neural Processing Letters
Parameter by Parameter Algorithm for Multilayer Perceptrons
Neural Processing Letters
A Hybrid Training Algorithm for Feedforward Neural Networks
Neural Processing Letters
International Journal of Advanced Media and Communication
An improved training algorithm for feedforward neural network learning based on terminal attractors
Journal of Global Optimization
A new cuckoo search based levenberg-marquardt (CSLM) algorithm
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
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In this letter, a new approach for the learning process of multilayer feedforward neural network is introduced. This approach minimizes a modified form of the criterion used in the standard backpropagation algorithm. This criterion is based on the sum of the linear and the nonlinear quadratic errors of the output neuron. The quadratic linear error signal is appropriately weighted. The choice of the weighted design parameter is evaluated via rank convergence series analysis and asymptotic constant error values. The new proposed modified standard backpropagation algorithm (MBP) is first derived on a single neuron-based net and then extended to a general feedforward neural network. Simulation results of the 4-b parity checker and the circle in the square problem confirm that the performance of the MBP algorithm exceed the standard backpropagation (SBP) in the reduction of the total number of iterations and in the learning time