网络函数

An adaptive control scheme,that employs a Gaussian radial basis function networkwith output weights updated on-line according to the Lyapunov stability theory,is sug-gested for regulation of a class of chaotic systems with uncertainties.

四、针对一类带有不确定性的混沌系统，提出基于高斯径向基函数网络的的自适应控制框架，其中网络输出权重根据Lyapunov稳定性理论自适应在线调节。

Neural networks ; multiple objective programming/fuzzy optimum selection ; topologic structure of network ; stimulation function ; adjustment of weights

神经网络；多目标规划／模糊优选；网络拓扑结构；激励函数；权重调整

The structure of the orthogonal function neural network is first introduced, and then, the principle of orthogonal neural network is analyzed by using Chebyshev orthogonal polynomials.

首先，本文提出了正交函数神经网络的网络结构，分析了利用Chebyshev正交多项式形成神经网络的机理。

It adopts the voltage stability of distribution system as the object function, and uses the improved particle swarm optimization algorithm which can trace dynamically the environment's change to make the real-time dynamically reconfiguration of distribution system, and proposes a new coded form which divides the particle module according to the topological structure of distribution system, and then avoids the convergence precociously via the community characters of DPSO.

它以配电网络电压稳定为目标函数，采用可以动态跟踪环境变化的改进粒子群算法对现有典型的辐射状网络进行动态实时重构，并提出了一种新的编码形式，按照系统的拓扑结构划分粒子种类，从而利用改进粒子群算法的社区特性避免过早收敛。

Finally, it has been proved that a step learning function, which satisfies the condition of LMS algorithm, could make network weight converge with probability 1 to the feature vector.

并证明了SOFM网络的学习存在一表征特征矢量的稳定解，最后证明了一个满足LMS随机逼近收敛算法条件的步幅函数可使网络权值以概率1收敛于特征矢量。

For the some classes of neural networks with time delays, by constructing proper vector Liapunov functions and nonlinear integro-differential inequalities involving both variable delays and unbounded delay, using matrix theory, sufficient conditions for absolute exponential stability are obtained.

对于几类具有变时间滞后和无穷时滞的人工神经网络，利用矢量Liapunov函数法，得到了的神经网络平衡点指数稳定的充分条件。

In view of network structure, FCP is equivalent to the radial basis function network.

在结构上，FCP网络同径向基函数网络是等价的。

Radical basis function neural networks have the powerful approximating capability, simple structure and outstandinglearning speed in comparison with other sorts of neural networks.

径向基函数神经网络具有其它前向神经网络所不具有的最佳逼近功能，并且结构简单，训练速度快。

This dissertation highlights neural network models and learning algorithms related, which are playing an equally important role in neural network research. The main results obtained are presented as follows: Reproducing kernel developed in different disciplines has become an importanttool in functional approximation.

神经网络模型和与之相适应的训练算法是本文研究的重点，它们在神经网络的研究中占有同样重要的位置，本文获得的主要结果如下：再生核源于不同学科分支，目前已成为函数逼近的重要工具。

The experimental results show that the predicting result of RBFNN is better than that of BPNN. Root mean square error of prediction decreases from 0.72 0Brix to 0.65 0Brix.

研究结果表明：径向基函数神经网络比反向神经网络获得的预测结果更好，校正模型的预测均方根误差从0.720Brix降低到0.650Brix。

Neither the killing of Mr Zarqawi nor any breakthrough on the political front will stop the insurgency and the fratricidal murders in their tracks.

在对危险的南部地区访问时，他斥责什叶派民兵领导人对中央集权的挑衅行为。

In fact,I've got him on the satellite mobile right now.

实际上 我们已接通卫星可视电话了