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泛化性能    
相关语句
  generalization performance
    The root mean square relative error, mean absolute relative error and maximize absolute relative error of SVM model generalization performance are 1.06%, 0.96% and 1.16%, respectively.
    对SVM多元非线性回归泛化性能进行测试,其均方根相对误差为1.06%,平均绝对相对误差为0.96%,最大绝对相对误差为1.16%。
短句来源
    In order to improve Support Vector Regression (SVR) learning ability and generalization performance,a joint optimization method for selecting features and SVR parameters is proposed.
    为提高支持向量回归算法的学习能力和泛化性能,提出了特征选择和支持向量回归参数的联合优化方法。
短句来源
    A fast learning hybrid algorithm of feedforward neural networks based on the regularized least squares is presented. The proposed algorithm can improve greatly the generalization performance and real -time abilities of the feedforward networks. The algorithm is applied to the identification of chaotic system with large noise.
    提出一种基于正则化最小二乘的前向神经网络快速学习的混合算法,极大地提高了前向网络的泛化性能和实时性能.应用该算法对存在较大噪声情形下的混沌系统进行辨识,辨识精度较高,收敛速度较快,辨识模型能较准确地逼近原混沌系统.辨识效果明显优于用BP算法所得到的模型.
    Principal component analysis method to improve generalization performance of radial basis function network and its application research
    改善径向基函数网络泛化性能的主成分分析法及应用研究
短句来源
    A Study for Improving Generalization Performance of Multilayer Feedforward Neural Network
    多层前馈神经网络泛化性能改进研究
短句来源
更多       
  generalization performance
    The root mean square relative error, mean absolute relative error and maximize absolute relative error of SVM model generalization performance are 1.06%, 0.96% and 1.16%, respectively.
    对SVM多元非线性回归泛化性能进行测试,其均方根相对误差为1.06%,平均绝对相对误差为0.96%,最大绝对相对误差为1.16%。
短句来源
    In order to improve Support Vector Regression (SVR) learning ability and generalization performance,a joint optimization method for selecting features and SVR parameters is proposed.
    为提高支持向量回归算法的学习能力和泛化性能,提出了特征选择和支持向量回归参数的联合优化方法。
短句来源
    A fast learning hybrid algorithm of feedforward neural networks based on the regularized least squares is presented. The proposed algorithm can improve greatly the generalization performance and real -time abilities of the feedforward networks. The algorithm is applied to the identification of chaotic system with large noise.
    提出一种基于正则化最小二乘的前向神经网络快速学习的混合算法,极大地提高了前向网络的泛化性能和实时性能.应用该算法对存在较大噪声情形下的混沌系统进行辨识,辨识精度较高,收敛速度较快,辨识模型能较准确地逼近原混沌系统.辨识效果明显优于用BP算法所得到的模型.
    Principal component analysis method to improve generalization performance of radial basis function network and its application research
    改善径向基函数网络泛化性能的主成分分析法及应用研究
短句来源
    A Study for Improving Generalization Performance of Multilayer Feedforward Neural Network
    多层前馈神经网络泛化性能改进研究
短句来源
更多       
  generalization ability
    Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
    支持向量机是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法。
短句来源
    In this paper,the fault diagnosis model is presented based on the fault-tolerance radial basis function neural network(RBF-NN)using ant colony optimization algorithm(ACOA). RBF-NN possesses excellent approaching ability,and its generalization ability can be further improved by ACOA.
    文中构造了基于蚁群优化算法(ant colony optimization algorithm,ACOA)的容错径向基函数神经网络(radial basis function neural network,RBF-NN)故障诊断模型,它具有强逼近能力,采用ACOA优化NN可进一步改善泛化性能
短句来源
    Target recognition algorithm for passive sonar system with high Generalization Ability
    具有高泛化性能的无源声呐目标识别算法
短句来源
    A New Algorithm to Improve the Generalization Ability of Feed-forward Neural Networks
    一种提高前向神经网络泛化性能的新算法
短句来源
    The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and having high generalization ability.
    支持向量机(Support Vector machine,简称SVM)是一种基于结构风险最小化原理,具有很高泛化性能的学习算法。
短句来源
更多       
  generalization capability
    Research on Learning and Generalization Capability of CMAC:An Overview
    CMAC学习性能及泛化性能研究综述
短句来源
    As for high precision control of uncertain nonlinear systems based on NN, the generalization capability of NN is so important that the control precision is affected directly by it .
    神经网络应用于控制系统设计主要是针对系统的非线性、不确定性和复杂性进行的。 对于基于神经网络的不确定非线性系统的高精度控制场合,神经网络泛化性能的好坏显得尤为重要,它直接影响到系统的控制精度。
短句来源
    Because more information is obtained, the combination network usually has better generalization capability than the best one.
    由于组合网络获得的信息量大,因此其泛化性能往往优于单个最佳子网络。
短句来源
    Thirdly, the weight and threshold of BP neural network model was optimized by genetic algorithm(GA), which has stronger macroscopic search and global optimization property, based on BP network model of the preparation of superfine quartz powder. This model is named GA-BP, and improves the generalization capability and the parameters forecast precision of BP network model, and was proved to be correct by both theoretical analysis and experiment.
    再次,本文以粉石英制备的BP网络模型为基础,利用遗传算法(GA)较强的宏观搜索能力和良好的全局优化性能,对BP网络模型的权值和阈值进行优化,极大地提高了BP网络模型的泛化性能和参数预测精度,将经过GA优化后的BP网络模型简称为GA-BP网络模型。
短句来源
    Finally, the method is used to model esterified ratios of polyethylene terephthalate (PET), the results show that it can calculate at higher speed with fewer fuzzy rules and better generalization capability, and achieve satisfactory prediction precision.
    仿真结果表明该方法运算速度快,模糊规则较少,同时具有的良好泛化性能,能够满足软测量建模精度的要求.
短句来源
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  generalization performance
In particular, they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically.
      
Theoretical analysis and experimental results show that the proposed algorithm can achieve better generalization performance.
      
The improved model is developed by the Levenberg-Marquardt training algorithm and the good generalization performance is demonstrated.
      
Generalization performance of multi-category kernel machines
      
The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods.
      
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