基于最小二乘法的SIMO傅里叶神经网络研究

利用傅里叶级数的原理，构造单输入、多输出(SIMO)傅里叶神经网络，将非线性映射转化成为线性映射，将求解神经网络权值的方法由非线性优化方法转化成为线性优化方法，并采用最小二乘法计算网络的权值，从而大大提高了神经网络的收敛速度并避免了局部极小问题.而且，在训练输出样本受白噪声影响时，最小二乘法具有良好的降低噪声影响的功能．

Research on SIMO Fourier Neural Networks Based on Least Square Method

Based on Fourier series principle, the single input, multiple outputs (SIMO) Fourier neural networks are proposed. The SIMO Fourier neural networks turn nonlinear mapping relationship into linear mapping relationship , turn the method of solving neural networks' weights from the nonlinear optimization method to linear optimization method, and use the least square method to compute the weights of the network. So, the SIMO Fourier neural networks highly improve the convergence speed and avoid local minima problem. When the training output samples are affected by white noise, the least square method have good denoising function.