双极柔性神经预测模型及其应用

Zhang Heng, Tao Huan-qi
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引用次数: 0

摘要

针对传统神经网络的缺陷,提出了一种包含柔性S参数变函数的柔性神经网络,以提高柔性神经网络的学习速度和泛化能力。挠性函数的作用函数称为s型函数,包含单极和双极。其中,采用双极柔性神经s型函数。给出了柔性神经网络的基本原理和学习算法。为说明所提柔性神经网络的有效性,给出了某电网负荷预测和太阳沱河洪水预测两个应用实例,结果表明该模型预测准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Bipolar Flexible Neural Forecasting Model and Its Application
A flexible neural network which includes flexible, S parameter-varying function is proposed owing to the defect of the tradition neural network in order to enhance the study speed and generalization of the flexible neural network. Action function of flexible function is called S-type function that contains monopole and bipolar. There, The bipolar flexible neural S-type function is adopted. It gives the basic principle of flexible neural network and learning algorithm. To illustrate the effectiveness of the proposed flexible neural network, we get two application examples, one is forecasting power load of a certain electric network and another is forecasting floods of Taiyangtuo Rever, the results show that the model is accurate in forecast.
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