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A method is proposed in order to accelerate the convergence of artifcial neural network modeling of chemical process,in which the principal components analysis is applied in the data processing in training sets.

文摘:达到提出应用主成分分析法对样本进行预处理,减少网络的输入因子数,消除输入因子间的相关性并简化网络结构,达到提高网络学习速率的目的,得到的人工神经网络模型能达到所要求的精度。

A novel method is proposed, which is based on orthogonal least square algorithm. This method offers a systematic way for the selection of input variables.

神经网络是进行负荷预测的一种成功方法,输入变量的选择是影响神经网络预测效果的重要因素,本文提出了基于正交最小平方方法的神经网络输入变量选择方法。

The structure of WNN is trained using Structural Risk Minimization based on statistical learning theory and weights of networks are optimized by adaptive orthogonal least square.

采用统计学理论的中的结构风险最小化原则为目标来训练网络的结构,采用自适应正交最小二乘法来训练网络权值,该方法最大限度地保证了网络的泛化能力。

Based on analyzing regression analysis, neural network and the least square support vector machines that the three soft computing methods fitting for data fitting, in the paper try to use nonlinear regression analysis, the BP neural network based on Levenberg-Marquardt training algorithm, radial basis function neural network on orthogonal least square training algorithm and least square support vector machines for tracking the position of glass plate.

本文在分析适于数据拟合的回归分析、神经网络、最小二乘支持向量机这三种"软计算"方法的基础上,尝试将非线性回归分析方法,基于Levenberg-Marquardt算法的BP神经网络,基于正交最小二乘训练算法的径向基函数神经网络和最小二乘支持向量机应用于玻璃板位的跟踪中。

The main works include five parts:(1) A modified K-means algorithm for optimizing the RBPNNs' structures was proposed;(2) The RBPNNs' structure optimization method based on the orthogonal least square algorithm was proposed that can greatly reduce the computation expense for the structure optimization;(3) Using the GA to perform the whole structure optimization of the RBPNNs was proposed in this thesis, which include simultaneous optimization of the hidden centers and the corresponding controlling parameters of the kernel functions.

主要工作体现在如下三个方面:(1)提出一种改进的 K-Means 算法;(2)提出了基于递推正交最小二乘算法的结构优化算法来训练径向基概率神经网络,从而大大减小了用于结构优化的计算开销;(3)提出使用遗传算法来实现径向基概率神经网络的全结构优化,即隐中心矢量和核函数控制参数同时优化,通过新设计的编码方式、新构造的适应度函数,充分发挥了 GA 的全局搜索性能,使得所优化的径向基概率神经网络的结构趋于最简。

Considering shortcomings of collecting network delay through SNMP, the system integrates some tools to measure performance accessing outer network including response time to outer server and available bandwidth of specified path.

为了弥补SNMP采集网络延迟方面的不足,系统集成一些工具主动测量到外部网络的服务性能,包括外部网络服务的响应时间以及指定路径上的可用带宽。

Sources have it that the incidence rate of intracranial gliomas postoperative reactional enhancement is 65.7%(134/204), whereas that of tumor residual is 34.3%(70/204) [1]. The network is capable of learning and memorizing, and can memorize the data diffusion features. Therefore, if data with such rates are employed to train the network, it will overstudy reactional enhancement. Even though the network training is not successful and all are diagnosed as reactional enhancement, the correctness will be still as high as 65.7%.

据文献报道,胶质瘤术后反应性强化的发生率是65.7%(134/204),肿瘤残存发生率是34.3%(70/204)[1],因为网络具有学习和记忆功能,能够记住数据的分布特点,因此用此比率的数据训练网络,对反应性强化会过度学习,即使网络训练不成功,全部诊断为反应性强化,正确率仍然高达65.7%。

The simulation results show the good performance for the system by using network to adjust the parameters and the recurrent weight of neural network on-line dynamically on the condition of variety of system parameter and the impact of outside uncertainty factors.

仿真结果表明,当系统参数动态变化或受到外部不确定因素影响时,利用神经网络来在线调整网络的隶属函数参数以及神经网络递归权值,使系统具有良好的动、静态性能。

Secondly, although the Article 10, clause 1 (12) of Chinese Copyright Law specifies RCIN with the characters of "the public","by wire or wireless","a place and at a time individually chosen" and "communicate work", it still not provides a clear and reasonable conception for the distinction to the other paratactic author's rights defined in the same article.

第二,尽管中国《著作权法》第十条第1款(12)将信息网络传播权定义为"向公众"、"以有线或者无线方式"、"在其个人选定的时间和地点"、"传播作品",但该条却没有对信息网络传播权的定义进行清晰合理的规定以区分信息网络传播权和该条列举的其它作者权利之间的不同。

The former firstly establishes state graph of power system, and then, with the use of the adjacency matrix and path matrix of the directed graph, fast finds out the key transmission sections suffering greatly from the outages of overload lines through simple calculation of matrix.

前者根据实时网络拓扑结构和潮流分布状态,在对初始电力网络简化、分区的基础上,利用有向图的邻接矩阵与路径矩阵,通过简单的矩阵运算,快速识别出与过载线路相关的关键输电断面;后者直接利用系统各支路的阻抗,从图论算法的基本思想出发,搜索最易遭受大负荷转移的关键传输路径,从而将安全性分析目标从整个网络缩小到某些关键的输电线路。

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