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The forecast model of patents granted in colleges based on genetic neural network

机译:基于遗传神经网络的高校专利授权量预测模型

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In order to avoid shortcomings of the standard BP network algorithm, the forecast model of patents granted in colleges is constructed based on genetic neural network. Involving the advantages of GA and BP, the algorithm can simultaneously complete genetic selection within a solution space to find the optimal points. Then the BP algorithm searchs the best optimal result from those points by the direction of negative gradient. Thus it not only can avoid the BP algorithm into a local minimum and slow convergence etc, but also can overcome long-search time, slow shortcomings of the GA caused by searching optimal solution in a similar form of exhaustive. Simulation indicates that the algorithm is more accuracy than the standard BP algorithm, faster incalculation and very well in applicability.
机译:为了避免标准BP网络算法的不足,基于遗传神经网络构建了高校专利授权的预测模型。利用GA和BP的优势,该算法可以在解决方案空间内同时完成遗传选择以找到最佳点。然后,BP算法按负梯度的方向从这些点中搜索最佳最佳结果。这样不仅可以避免BP算法陷入局部极小,收敛速度慢等问题,而且可以克服搜索时间长,GA搜索速度慢等缺点。仿真表明,该算法比标准BP算法具有更高的准确性,更快的计算速度和很好的适用性。

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