This paper describes an algorithm for constructing a single hidden layer feedforward neural network. A distinguishing feature of this algorithm is that it ...
This approach is only applicable if the output unit activations are determined by the hidden unit activations and the connection weights between the hidden ...
An algorithm for constructing a single hidden layer feedforward neural network that uses the quasi-Newton method to minimize the sequence of error functions ...
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The quasi-Newton training method is the most effective method for feed-forward neural networks with respect to the training precision.
Oct 22, 2024 · This paper examines the effectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting.
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Dec 29, 2016 · Newton's method, a root finding algorithm, maximizes a function using knowledge of its second derivative. That can be faster when the second ...
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[PDF] Training the random neural network using quasi-Newton methods
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This algorithm uses gradient-descent of a quadratic error function to determine the parameters of the net- work (excitation and inhibition weights). The.
An optimal set of weights for this network is obtained by applying a variant of the quasi-Newton method for unconstrained optimisation. If this set of weights ...
The quasi-Newton training method is the most effective method for feed-forward neural networks with respect to the training precision.
This paper examines the e ectiveness of using a quasi-Newton based training of a feedfor- ward neural network for forecasting. We have developed a novel quasi- ...
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