用G-N法求解非线性最小二乘优化问题 用修正G-N法求解非线性最小二乘优化问题 用L-M法求解非线性最小二乘优化问题 -GN method for solving nonlinear least squares optimization problems using a modified GN method for solving nonlinear least squares optimization problems using LM method for solving non

支持向量机模型用于预测分析效果显著，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中-Support vector machine model is used to predict significant effect in solving small sample, nonlinear and high dimensional pattern recognition exhibit many unique advantages, and

基于BP神经网络的非线性分类，及运用其解决异或问题，并对Iris数据进行分类-Nonlinear classification based on BP neural network and the xor problem, and its appplication on the data of Iris.

CEEMDAN方法，用于解决EMD分解过程中的模态混叠问题，同常用的EEMD方法相比，其有效减少了迭代次数，增加了重构精度，更加适合非线性信号的分析。(CEEMDAN method is used to solve the modal aliasing problem in the EMD decomposition process. Compared with the commonly used EEMD method, it effectively reduces the number of