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Then the BP neural network trained by this algorithm is applied into function approximation.

并将算法训练后的BP神经网络运用到函数逼近中去。

The random spline plays very important role in the approximation and fitting of random function.

随机样条在对随机函数的逼近与拟合中都有重要作用。

Nonlinear function approximation using BP neural network and RBF neural network is simulated.

最后,作了用BP网络和RBF网络逼近非线性函数的仿真研究,并作了比较。

It is shown that the Chebyshev polynomial approximation is a valid method for the investigation of non-smooth systems.

研究表明,Chebyshev多项式逼近也是研究随机非光滑系统动力学行为的一种有效方法。

The control of the operative mechanism adopts the method of high-speed approximation.

对执行机构的控制采用了快速逼近方式。

To overcome this problem,the paper presents a non-linear approximation method to control the optimized bandeletization.

为了解决这个问题,提出了一种通过非线性逼近来控制最佳Bandelet化过程的方法。

The polynomial approximation method is also employed to obtain simple formulas for frequency and amplitude correction.

本文利用多项式逼近方法获得了频率和幅值修正的计算公式。

Proposed in the paper are the ordered monotony property of MFNNs when the transfer function is monotone, the continuous mapping theorem, and non approximation theorem to function.

给出了单调传递函数的 MFNNs按序单调特性、连续映射定理以及非函数一致逼近定理。

The proof relies on regular approximation, the fixed point theorem, the embedding theorems, the semigroup theory and a series of complicated estimates.

然后,利用此结果,通过区域正则化逼近及半群理论获得各种估计,并给出了非齐次Liouville-Poisson模型的整体古典解。

Using the stochastic approximation theory, a rigorous analysis of the convergence properties of the algorithm was presented.

然后,采用随机逼近理论,严格分析了算法的收敛性。

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