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这是一个matlab实现第归神经网络的例子, 实现的第归神经网络是 : Real Time Recurrent Learning-This is a Matlab achieve its neural network naturalization example, the realization of naturalization neural network is : Real Time Learning Recurrent
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a transmission line fault location model which is based on an Elman recurrent network
(ERN) has been presented for balanced and unbalanced short circuit faults. All fault situations with
different inception times are implemented on a 380-kV proto
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Key Features
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and laye
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Neural Network LAM. A number of procedures are described for finding delta E/ delta W/sub ij/ where E is an error functional of the temporal trajectory of the states of a continuous recurrent network and w/sub ij/ are the weights of that network-Neur
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Neural Network Applet
Learning examples on feedforward networks
recurrent Backward Propagation
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A Hopfield network is a form of recurrent artificial neural network invented by John Hopfield in 1982. Hopfield nets serve as content-addressable memory systems with binary threshold nodes. They are guaranteed to converge to a local minimum, but conv
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matlab identification of cht
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Elman神经网络：Elman网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的, 它是一种典型的局部回归网络( global feed for ward l ocal recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的前向神经网络。Elman网络具有与多层前向网络相似的多层结构。它的主要结构是前馈连接, 包括输入层、 隐含层、 输出层, 其连接权可以进行学习修正 反馈连接由一组“结构 ” 单元构成,用来记忆前一时刻的输出值, 其连接权值是固
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For neural network Recurrent for machine
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Artificial neural network for Recurrent
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recurrent method for neural network
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Elman神经网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的，是一种典型的局部回归网络( global feed forward local recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的递归神经网络。(The Elman neural network, which was first proposed by j. l. Elman in 1990 for voice processing, is a typical global f
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循环神经网络学习流量预测lstm适用于初学者学习使用(Recurrent neural network learning traffic forecast)
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一种新型的循环神经网络（RNN），在时序预测领域有广泛应用前景(A new type of recurrent neural network (RNN) has a wide application prospect in the field of time series prediction)
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