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Selection of number of hidden neurons in neural networks in renewable energy systems

机译:可再生能源系统中神经网络中隐藏神经元数量的选择

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This paper presents a new approach to select number of hidden neurons in neural network in renewable energy systems. The random selection of number of hidden neurons might cause over fitting and under fitting problems in neural networks. The proper selection of neurons in hidden layer is important in the design of neural network model. To fix hidden neurons, 91 various criteria are examined based on estimated mean squared error. The convergence analysis is performed for the various proposed criteria-. To verify the effectiveness of the proposed model, simulations were conducted on real time wind data. Results show that with minimum error the proposed approach can be used in renewable energy systems.
机译:本文提出了一种在可再生能源系统中选择神经网络中隐藏神经元数量的新方法。隐藏神经元数量的随机选择可能会导致神经网络的过度拟合和拟合不足问题。隐层神经元的正确选择在神经网络模型的设计中很重要。为了修复隐藏的神经元,基于估计的均方误差检查了91种不同的标准。针对各种提议的标准执行收敛分析。为了验证所提出模型的有效性,对实时风数据进行了仿真。结果表明,该方法可将误差最小,可用于可再生能源系统。

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