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In this paper, second order algorithms, such as Levenberg Marquardt algorithm, are recommended for neural network training. Being different from traditional computation in second order algorithms, the proposed method simplifies Hessian matrix computation, by removing Jacobian matrix computation and storage. Matrix multiplications are replaced by vector operations. The proposed computation not only...
The method introduced in this paper allows for training arbitrarily connected neural networks, therefore, more powerful neural network architectures with connections across layers can be efficiently trained. The proposed method also simplifies neural network training, by using the forward-only computation instead of the traditionally used forward and backward computation.
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...
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