【摘 要】
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A relaxation least squares-based learning algorithm for neural networks is proposed. Not only does it have a fast convergence rate, but it involves less computa
【出 处】
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Journal of Systems Engineering and Electronics
论文部分内容阅读
A relaxation least squares-based learning algorithm for neural networks is proposed. Not only does it have a fast convergence rate, but it involves less computation quantity. Therefore, it is suitable to deal with the case when a network has a large scale but the number of training data is very limited. It has been used in converting furnace process modelling, and impressive result has been obtained.
A not least does it have a fast convergence rate, but it requires involves less computation quantity. Therefore, it is suitable to deal with the case when a network has a large scale but the number of training data is very limited. It has been used in converting furnace process modeling, and impressive result has been obtained.
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