Neural Networks and Unconstrained Optimization
作者： L. C. W. Dixon
DOI: 10.1007/978-94-009-0369-2_19
关键词: Algorithm 、 Radial basis function network 、 Unconstrained optimization 、 Computer science 、 Stochastic neural network 、 Radial basis function 、 Function (mathematics) 、 Backpropagation 、 Artificial neural network 、 Parallel processing (DSP implementation)
摘要: When performing the unconstrained optimisation of a complicated industrial problem, main computational time is usually spent in calculation objective function and its derivatives. The performed sequentially, so if parallel processing machine to be used, then either number evaluations must calculated or sequential replaced by calculation.
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