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改进rbf神经网络
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  improved rbf network
     Nonlinear Time Series Prediction Based on Improved RBF Network
     基于改进RBF神经网络的非线性时间序列预测
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  “改进rbf神经网络”译为未确定词的双语例句
     Oxidation tower yield model based on modified RBF neural network
     基于改进RBF神经网络的氧化塔产品收率模型
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     Application of an Improved RBF Neural Network in the Prediction of Variation of Groundwater Level
     改进RBF神经网络在地下水动态预报中的应用
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     PRECIPITATION PREDICTING BASED ON IMPROVED RADIAL BASIS FUNCTION NEURAL NETWORKS
     改进RBF神经网络用于降雨量预测
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     to further support the view that chaos exist in EEG signals. And the result of prediction of RBF neural network and NLCP indicate the effect of RBF neural network prediction is better than that of nonlinear prediction; the method of NLCP is adaptive to time series with strong periodic components.
     随后对该脑电动力学模型进行非线性预测、径向基函数(Radial Basis Functions,RBF)神经网络预测和非线性正交预测(Nonlinear Cross-Prediction,NLCP),从预测结果中,发现改进RBF神经网络预测的效果要好于非线性预测,并且NLCP方法对含有强周期分量的高维系统具有较好的适用性。
短句来源
     When radial basic function (RBF) is applied to power load forecasting, if the input space is heavily self- correlated and the input numbers are too many, in that case too much centres of the neurons will be overlapped, finally the accuracy of load forecasting by RBF network will be descendent.
     径向基函数(RBF)神经网络应用于电力系统负荷预测时,如果输入空间严重自相关及网络维数较高时,RBF神经网络的预测精度会下降。 针对这一问题,文中提出了一种应用于电力负荷预测的改进 RBF 神经网络新方法。
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  相似匹配句对
     It improves the result of N.
     改进了N .
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     The Improved RBF Neural Network and Its Application
     改进RBF神经元网络及其应用
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     Improve and application of RBF neural network
     对RBF神经网络改进及应用
短句来源
     Improved RBF Neural network and its application
     改进RBF神经网络及其应用
短句来源
     PRECIPITATION PREDICTING BASED ON IMPROVED RADIAL BASIS FUNCTION NEURAL NETWORKS
     改进RBF神经网络用于降雨量预测
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Generally speaking, the classical Bayes classification methods must hypothesize what distribution a random variable be subject to before analyzing. It is impossible to get a high correct rate if the selected model is not agreement with the true one. The statistic approaches, for example, distance, Fisher, k nearest neighbor, wise linear classifiers, fail to solve multi regional distributions such as the alternate table problems. Not only does the two spiral problem give a challenge to the statistic...

Generally speaking, the classical Bayes classification methods must hypothesize what distribution a random variable be subject to before analyzing. It is impossible to get a high correct rate if the selected model is not agreement with the true one. The statistic approaches, for example, distance, Fisher, k nearest neighbor, wise linear classifiers, fail to solve multi regional distributions such as the alternate table problems. Not only does the two spiral problem give a challenge to the statistic methods again, but also brings a doubt about the abilities of the general feedforward multi layered LBF neural networks. This paper presents an adaptive algorithm of optimally determining the structures, number, positions and widths of kernel functions of the improved radial basis function(IRBF) neural networks. The algorithm has approximately computational complexity comparing with the back propagation fellow used in the general feedforward three layered LBF networks. Whether or not a kernel function comes into being depends on the relationships between some misclassification patterns and their neighbor classes. The extreme importance of the two layered LBF networks is testified by many experiments. Whether or not a kernel be finally continued to have is determined by its contribution to improve the classification correct rate of the test set. A lot of applications show this kind of IRBF networks have advantages over the feedforward three layered RBF and LBF ones at such aspects as convergence rates and classification precision, achievements of optimal structures, capabilities of getting rid of local points. This kind of networks are able to work well in a real time way.

经典的 Bayes分类方法一般需要事先对样本的分布特性作出假设 ,当假设模型与样本实际分布情况不相符时 ,就难以得到较高的分类精度。当处理同类别多区域样本分布问题 ,例如变标签问题时 ,距离判别、Fisher判别、k-近邻分类、分段线性分类等统计分析方法遇到困难。双螺旋问题不仅使统计方法受到挑战 ,更使人们对一般前向多层神经网络的能力提出疑问。本文提出了改进的 RBF神经网络结构、核函数个数、位置与宽度优化算法。该算法的计算复杂性与一般前向三层LBF网络所用的误差反传算法大致相同。核函数生成既考虑了训练集样本自身的类别因素 ,又考虑了错分样本与邻近类别的关系。一个核函数的最终保留与否根据其对提高测试集分类正确率的贡献大小来决定。同时实验验证了两层 LBF网络对提高改进的 RBF网络分类正确率的极端重要性。大量应用实例表明 ,与前向三层 RBF网络和前向三层 LBF网络相比 ,该 IRBF网络具有收敛速度快、分类精度高、易于得到最小结构、在学习过程中不易陷入局部极小点等优点 ,有利于实现实时分析

This paper presents a new improved RBF neural network.With the improved network,the cooling HNO 3 system has been successfully modeled.

提出了一种改进的 RBF神经网络 ,并利用改进的 RBF作为故障诊断模型对某高温硝酸冷却系统进行了故障建模研究 ,仿真表明是可行和有效的。

Predicating the developing trends of a process and monitoring it on the basis of the analysis of measured data has long been a subject drawing wide attention of scholars at home and abroad. Considering the high training speed of RBF neural networks, a method based on a two-stage RBF neural network is proposed for the dynamic monitoring of chemical processes. The first stage is used to predicate the future variable values in the coming time, the second is used to forecast the faults. For the purpose of achieving...

Predicating the developing trends of a process and monitoring it on the basis of the analysis of measured data has long been a subject drawing wide attention of scholars at home and abroad. Considering the high training speed of RBF neural networks, a method based on a two-stage RBF neural network is proposed for the dynamic monitoring of chemical processes. The first stage is used to predicate the future variable values in the coming time, the second is used to forecast the faults. For the purpose of achieving reliable monitoring effects with limited samples, some measures are proposed to improve the interoperation performance of the RBF neural network, together with a transformation acting on the output of the second stage to determine the possibilities of the faults more accurately. They were applied to the dynamic monitoring for a distillation tower. The results showed a great success.

通过分析测量数据预测过程发展趋势,进而对过程实行监控,早已成为国内外学者所关心的热点课题.根据 RBF神经网络训练速度快的特点,提出将其用于化工过程的动态监控.第一级网络用于预测未来一时间段内的有关状态量,第二级网络根据预测结果判断是否将会发生事故.为了在有限样本条件下取得较可靠的监控效果,提出了改进RBF神经网络插值性能的措施,并提出对第二级网络输出结果进行变换以准确确定事故可能性的方法,以上方法被用于蒸馏塔开工过程的动态监控,结果令人满意.

 
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