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Approximation of algebraic and trigonometric polynomials by feedforward neural networks
ISSN号:0941-0643
期刊名称:Neural Computing & Applications
时间:2012.5.5
页码:73-80
相关项目:基于L1/2正则化的压缩传感可重构性理论研究
作者:
Wang Jianjun|Chen Baili|Yang Chanyun|
同期刊论文项目
基于L1/2正则化的压缩传感可重构性理论研究
期刊论文 19
会议论文 2
同项目期刊论文
Estimation of Approximation with Jacobi Weights by Multivariate Baskakov Operator
稳健Lq正则化理论: 解的渐近分布与变量选择一致性
Bernstein型算子加Jacobi权高阶逼近的特征刻画
Bernstein型算子高阶逼近的特征刻划
Bernstein型算子线性组合加Jacobi权逼近及高阶导数的等价定理
CONSTRUCTIVE ESTIMATION OF APPROXIMATION FOR TRIGONOMETRIC NEURAL NETWORKS
Derivatives of Multivariate Bernstein Operators and Smoothness with Jacobi Weights
Estimation of approximating rate for neural networks in L(w,p)
Approximation Order for Multivariate Durrmeyer Operators with Jacobi Weights
On recovery of block-sparse signals via mixed l2/lq norm minimization
多分类最大间隔孪生支持向量机
Lp error estimate for minimal norm by SBF interpolation
ξ-α estimator for fuzzy support vector machine
Neural networks and the best Trigonometric approximation
A note on block-sparse signal recovery with chherent tight frames
基于混合l2/l1范数极小化方法的块稀疏信号重构条件
NEURAL NETWORKS AND THE BEST TRIGOMOMETRIC APPROXIMATION