以最佳多项式逼近为度量，用构造性方法估计单隐层神经网络逼近连续函数的速度。
With the best polynomial approximation as a metric, the rate of approximation of the neural networks with single hidden layer to a continuous function is estimated by using a constructive approach.
应用第一多项式系列的线性组合构成的某连续函数的最佳逼近函数，具有一致逼近的性质。
The optimal approximation functions, of a continuous function which are constituted of the linear combination of series of Chebychev polynome have the characteristics of uniform approximation.
这种方法能够保证辨识出的参数是最佳的；而且不用求解对应的非线性最小二乘问题，只需求一元多项式的根，从而大大减少计算量。
This approach can guarantee that the identified parameters are optimal, by solving a one-dimensional polynomial equation instead of a nonlinear least square problem.
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