A new supervised learning algorithm for multilayered and interconnected neural networks
IEEE Transactions on Neural Networks
An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons
IEEE Transactions on Neural Networks
A new wide range Euclidean distance circuit for neural network hardware implementations
IEEE Transactions on Neural Networks
New dynamical optimal learning for linear multilayer FNN
IEEE Transactions on Neural Networks
Efficient training algorithms for a class of shunting inhibitory convolutional neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification
IEEE Transactions on Neural Networks
Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks
IEEE Transactions on Neural Networks
Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
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The paper is going to introduce a revised C++ version of neural network trainer (NNT) which is developed based on neuron by neuron computation. Besides traditional error back propagation (EBP) algorithm, two improved version of Levenberg Marquardt (LM) algorithm and a newly developing algorithm are also implemented. The software can handle not only conventional multilayer perceptron networks, but also arbitrarily connected neuron networks. Comparing with the original NNT developed based on MATLAB [18], the revised version can handle much larger networks and the training speed is also improved as 50 to 100 times faster. Several practical applications are presented to show the power of this training tool. The software is available for everyone on the website.