A new architecture of neural network

Yongjun Zhang, Zongzhi Chen
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引用次数: 7

Abstract

Describes a novel neural-network architecture, the neural network loop (NNL), and its learning rules. It can operate as Hopfield, BAM (bidirectional associative memory), and other kinds of neural networks. In particular, it can perform multiple category associative memory. This capability is very similar to that of the human brain. It can be applied to pattern recognition and associative memory. Computer simulation was carried out, and the results prove that NNL is an effective network.<>
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一种新的神经网络结构
描述了一种新的神经网络结构,神经网络环路(NNL),以及它的学习规则。它可以像Hopfield、BAM(双向联想记忆)等神经网络一样工作。尤其具有多类别联想记忆功能。这种能力与人类的大脑非常相似。它可以应用于模式识别和联想记忆。计算机仿真结果表明,神经网络是一种有效的网络。
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Control of a robotic manipulating arm by a neural network simulation of the human cerebral and cerebellar cortical processes Neural network training using homotopy continuation methods A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures The abilities of neural networks to abstract and to use abstractions Backpropagation based on the logarithmic error function and elimination of local minima
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