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Shapes Of Nonmonotonic Activation Functions In A Chaotic Neural Network Associative Memory Model And Its Evaluation

机译:混沌神经网络联想记忆模型中非单调激活函数的形状及其估计

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The purpose of this paper is to investigate the performance of the associative memory model using Aihara's chaotic neural network with different activation functions. Sigmoid function, a monotonic function, was used in Aihara's original model. However, in the static associative memory, it is reported that the storage capacity of the network is improved when a nonmonotonic function is used as the activation function. To improve the associative ability of chaotic neural network, kinds of nonmonotonic functions have been proposed to serve as activation function. This paper investigates their difference as to retrieval ability, and proposes an advanced nonmonotonic function. By computer simulation, we discuss what kind of shape is good regarding improving the associative ability of chaotic neural network.
机译:本文的目的是研究使用具有不同激活函数的Aihara混沌神经网络的联想记忆模型的性能。 Sigmoid函数是一种单调函数,用于Aihara的原始模型。然而,据报道,在静态联想存储器中,当将非单调函数用作激活函数时,网络的存储容量得到了提高。为了提高混沌神经网络的结合能力,提出了多种非单调函数作为激活函数。本文研究了它们在检索能力方面的差异,并提出了一种高级的非单调函数。通过计算机仿真,我们讨论了哪种形状对改善混沌神经网络的关联能力是好的。

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