Connectionist learning procedures
Machine learning: paradigms and methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
A New Learning Algorithm Using Simultaneous Perturbation with Weight Initialization
Neural Processing Letters
A generalized learning paradigm exploiting the structure of feedforward neural networks
IEEE Transactions on Neural Networks
Extended least squares based algorithm for training feedforward networks
IEEE Transactions on Neural Networks
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
Dynamic tunneling technique for efficient training of multilayer perceptrons
IEEE Transactions on Neural Networks
A new supervised learning algorithm for multilayered and interconnected neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A general backpropagation algorithm for feedforward neural networks learning
IEEE Transactions on Neural Networks
Neighborhood based Levenberg-Marquardt algorithm for neural network training
IEEE Transactions on Neural Networks
Identification and modeling for non-linear dynamic system using neural networks type MLP
Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to increase Digital Citizenship
An improved training algorithm for feedforward neural network learning based on terminal attractors
Journal of Global Optimization
An improved three-term optical backpropagation algorithm
International Journal of Artificial Intelligence and Soft Computing
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In this paper, a new efficient learning procedure for training single hidden layer feedforward network is proposed. This procedure trains the output layer and the hidden layer separately. A new optimization criterion for the hidden layer is proposed. Existing methods to find fictitious teacher signal for the output of each hidden neuron, modified standard backpropagation algorithm and the new optimization criterion are combined to train the feedforward neural networks. The effectiveness of the proposed procedure is shown by the simulation results.