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In terms of the positivity, compact support and normalization of B-spline basic function, the adjustment of the weights are accomplished locally and the output is simple. The results of the experiments show that the structure of the B-spline neurofuzzy-networks is clear and the training is more speedily than that of BP network.

在对刀具磨损状态进行监测过程中可以看出,B样条模糊神经网络内部的连接结构透明,可以很方便的根据实际需要调节网络结构,进行网络的优化来得到更加理想的诊断结果,同时证明了B样条模糊神经网络的训练速度优于BP网络。

In this paper chip shapes were recognized by using of radbas neural network. We put forward area ratio feature, Euler number feature, etc. geometry feature of chip shape image and thing of those features as inputting vector of neural network. Adopt radbas neural network and training the network using RLS.

研究了径向基函数神经网络在硬质合金刀具切屑形态图像识别中的应用,提出了面积比、欧拉数、分散度等硬质合金刀具切屑形态图像的几何特征,以上述特征作为神经网络的输入矢量,利用径向基函数网络,采用了递推最小二乘法训练该网络。

By using the Lyapunov functional method and matrix theory, the critical dynamics behavior of recurrent neural networks with nearest point projection has been investigated. It is shown that such networks are globally convergent and asymptotically stable when the networks are nonlinear non-dilatation.

基于Lyapunov泛函方法和矩阵理论,本文研究一类被广泛应用的反馈神经网络―最近点投影型反馈神经网络的临界全局动力学性态,当网络具有非线性不扩张性质时,在临界条件下得到该类网络全局收敛性和渐近稳定性。

The experimental results indicate that the LMBP neural network has higher classification rate, more rapidly converge velocity by comparing with other improved BP algorithm and the Radial Basis Function neural network. Through the experiments of recognizing the sonar image polluted with Gaussian noise, it is proved that the LMBP neural network has good stability and better anti-noise performance.

通过与标准BP算法和几种常用改进型BP神经网络以及径向基函数网络比较,验证了用LMBP神经网络作为声纳图像识别系统中的分类器,能够提高图像的识别率,加快网络的收敛速度,通过对受不同程度高斯白噪声污染的声纳图象的识别,验证了其性能稳定,具有较好的抗噪声性能。

The fault diagnosis of Fuzzy ANN is realized by the Neural Network Toolbox of MATLAB. The result is proved valid, but also proved easy to be trapped in the local extremum.

这里以BP网络为应用模型,把模糊化后的故障特征参数作为网络的输入样本,对样本数据训练、仿真,应用MATLAB提供的神经网络工具箱函数实现了模糊神经网络对设备故障的诊断,结果证明了该诊断模型的有效性,但也暴露出BP算法易陷入局部极值的弱点。

At last, the application of above modeling and control strategies has been demonstrated by the simulations of a nonlinear dc motor control problem. Under this control strategy, system identification phase is not needed, and only a controller based on the recurrent neural network is provided. Because the control and learning laws are designed with stable theory, stability and convergence problems are solved here with rigorous analysis, which can not been analyzed easily when usual BP neural networks are used.

最后通过非线性电机模型的仿真研究,表明了基于上述动态动态递归神经网络模型对非线性系统进行自适应控制的实际可行性,并且采用这种方法不需要对系统进行离线辨识,整个系统只采用了一个基于动态递归神经网络的控制器,由于整个系统是基于稳定性理论设计的,有效地解决了应用BP网络等其它前向网络时系统的稳定性和收敛性难以分析的问题。

Structure-Based Approximation Learning Theory: Under the description of manifold and topological architecture of neural field, we have proposed a learning frameworks of neural networks based on global architecture approximation theory borrowed from the concept of topology and manifold to solve the optimal problems of learning in non-nonlinear and non-Euclidean space, and discussed a few basic components of the learning theory, learning mechanism and learning algorithm in the term of structure-based approximation and global structure analysis.

基于结构的逼近学习理论:通过人工神经网络模型神经场的流形结构和拓扑结构描述,我们提出了在非线性和非欧氏空间中基于整体结构逼近的神经场学习理论框架;用对偶流形理论和拓扑概念作为工具,讨论了神经网络的学习理论、学习机理、学习算法和算法的具体实现。

Throught adaptive algorithm of network, the system constantly revise right values distribute so as to meet goal. Distribute the heuristic knowledge and the experience knowledge to network interconnection and right value.?The reasoning machine carries on reasoning according to the knowledge that involve in knowledge base and fact that users offer.?Through front computation of nerve network, the output port obtains the output vector.?The explanation module of ES is explained to user.At last, this paper has realized grab hydraulic system based on ANNES intelligent diagnosis, it has proven this fault diagnosis method feasibility.

ANNES的工作过程是:①首先建立知识库;②学习系统获取知识时,它只要求专家提供范例及相应的解,通过神经网络学习算法对样本进行学习,经过网络内部自适应算法不断修改权值分布以达到要求,把专家求解实际问题的启发式知识和经验分布到网络的互连及权值分布上;③推理机根据知识库中的知识和用户提供的事实进行推理;④通过神经网络的前向计算,在输出端获得输出矢量:⑤经专家系

Fuzzy neural network is used in the three phase adaptive reclosure so that the reclose of permanent faults can be avoided. A multi-input fuzzy neural network is constructed and the algorithm of network is designed. Taking the virtual value of three phase voltage as the input with its scale changed, the network learning can be completed with convergence reached after the process of fuzzification, normalization and perspicuity, so it can disinguish transient faults from permanent faults.

为了避免重合闸重合于永久性故障,将模糊神经网络应用于三相自适应重合闸中,构造了一个多输入的模糊神经网络,设计了网络的算法,将三相端压有效值作为输入,经尺度变换、模糊化、归一化和清晰化,并利用梯度下降法修正误差,使得网络完成学习并最终收敛,在输入故障数据时能根据网络的输出结果准确地判别瞬时性故障与永久性故障。

In both sampling period and fractal computing dimension,the signal is fuzzed to construct fractal dimension subordinating degree. A novel correct method,the fuzzy neural network diagnosis method,was proposed,in which the input is the fractal dimension subordinating degree, and the output is the unit matrix. A gearing system was diagnosed by using the modeling.

以多时间尺度采样所获得的分形维数隶属度作为网络输入,单位矩阵为网络输出的分形模糊神经网络,建立了时域精确诊断新方法,通过对典型齿轮系统故障进行精确诊断,结果表明分形模糊神经网络诊断方法的有效性。

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