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首页> 外文期刊>Electric power systems research >Superfast autoconfiguring artificial neural networks and their application to power systems
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Superfast autoconfiguring artificial neural networks and their application to power systems

机译：超快速自动配置人工神经网络及其在电力系统中的应用

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摘要

This paper presents the new generation of artificial neural networks (ANNs) for solving the task of power system operation planning. Today the error back-propagation ANNs are used most because of their simplicity and the possibility of parallel implementation on neuro-computers for high-speed execution. In spite of their popularity they have two major drawbacks: the learning process is time consuming and there is no exact rule for setting the number of neurons to avoid overfitting or underfitting and to achieve, hopefully, a converging learning phase. To avoid these difficulties, a new generation of ANNs has been developed based on the theory of radial basis functions for approximations. A comparison test on an actual problem in power system operation was performed. The results show that this new algorithm is superior to back-propagation ANNs and optimal configured back-propagation ANNs achieved with genetic algorithms.
机译：本文提出了用于解决电力系统运行计划任务的新一代人工神经网络（ANN）。如今，由于其简单性以及在神经计算机上并行执行以实现高速执行的可能性，大多数使用了误差反向传播ANN。尽管它们受欢迎，但它们有两个主要缺点：学习过程很耗时，并且没有确切的规则来设置神经元的数量，以避免过拟合或欠拟合，并希望达到收敛的学习阶段。为了避免这些困难，基于径向基函数的近似理论已经开发了新一代的人工神经网络。对电力系统运行中的实际问题进行了比较测试。结果表明，该新算法优于反向传播的人工神经网络和遗传算法实现的最优配置的反向传播的人工神经网络。

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