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首页> 外文期刊>Applied Soft Computing >Multilayer feed forward neural networks for non-linear continuous bidirectional associative memory
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Multilayer feed forward neural networks for non-linear continuous bidirectional associative memory

机译：多层馈送非线性连续双向关联内存的前向神经网络

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摘要

The study of Bidirectional associative memory (BAM), with recurrent neural networks and symmetric as well as asymmetric weights, has already been undertaken in various different ways. Using two phases of learning for multilayer neural network architecture in the present paper, a multilayer feed forward neural network model has been proposed to construct the non-linear continuous BAM for pattern association. In the first phase an input pattern is presented to input layer and back propagation learning rule is used to train the network in the feed forward direction for the corresponding associated output pattern. In second phase the output pattern is presented to output layer as input and again the back propagation learning rule is used to train the same network in feedback direction for the corresponding associated input pattern. In these two passes i.e. forward pass and backward pass, the interconnection weights are considered asymmetric. This training process continues till the network does not converge to the final optimal weights by minimizing the mean square errors in both the directions simultaneously. At this convergence of weights the input and output layers exhibit the stability and the performance of such type of BAM is evaluated for the test pattern set while the simulation results exhibit the better performance of associative memory for the proposed method. The storage capacity of network is also increased due to the non-linear mapping between input-output pattern pairs and also the occurrence of spurious states reduces during the recalling process. (C) 2017 Elsevier B.V. All rights reserved.
机译：具有经常性神经网络和对称的双向关联存储器（BAM）的研究已经以各种不同的方式进行了对称的神经网络和对称的权重。在本文中使用两相的学习，已经提出了一种多层馈送前向神经网络模型来构造用于模式关联的非线性连续BAM。在第一阶段中，将输入图案呈现给输入层，并将反向传播学习规则用于训练网络向前向向前方向训练相应的相关输出模式。在第二阶段中，输出模式被呈现为输出层作为输入，再次回到后传播学习规则用于训练相应的相关输入图案的反馈方向上的相同网络。在这两个通过中，即向前通过和向后通过，互连权重被认为是不对称的。该训练过程继续，直到网络不会通过同时在两个方向上最小化平均方误差来收敛到最终的最佳权重。在该权重的这种收敛下，输入和输出层表现出稳定性，并且在仿真结果表现出拟议方法的关联存储器的性能更好的性能时，评估这种类型的BAM的性能。由于输入输出模式对之间的非线性映射，网络的存储容量也增加，并且在回忆过程中，伪状态的发生也减少了杂散状态。 （c）2017 Elsevier B.v.保留所有权利。

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