在传统混沌神经网络模型的基础上，提出了一种具有衰减混沌噪声的混沌模拟退火神经网络模型(CSA - DCN)。

Based on deeply discussing the principle of chaotic neural network model, the chaotic simulated annealing model with decaying chaotic noise (CSA-DCN) is presented.

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本文利用模糊神经网络的通用逼近性，证明了在噪声情况下利用有限规则数的模糊神经网络可以以最小的误差预测混沌系统。

The thesis uses fuzzy neural network' s approach ability to prove that a fuzzy neural network with a finite rule number can optimally predict a chaotic system with noise.

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时滞、参数不确定性和随机噪声都将在相当大的程度上影响神经网络的整体性能，产生振荡行为或其它失稳现象甚至出现混沌现象。

So, time delays, parameter uncertainty and stochastic disturbances may affect the stability of the system, even lead to instability, oscillation or chaos phenomena.

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时滞、参数不确定性和随机噪声都将在相当大的程度上影响神经网络的整体性能，产生振荡行为或其它失稳现象甚至出现混沌现象。

So, time delays, parameter uncertainty and stochastic disturbances may affect the stability of the system, even lead to instability, oscillation or chaos phenomena.

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