模糊支持向量机文档，将模糊理论与支持向量机相结合-FSVM documents, the fuzzy theory and support vector machines combine

利用支持向量机对T-S型模糊系统建模的方法，结合BP算法对参数进行优化，从一定程度上解决模糊系统建模所存在的模型结构复杂、维数灾、泛化能力不强和实时性差等问题。-this paper analyzed the approach that applied support vector machines to create novel model in the T-S fuzzy system, combined with BP algorithm which could optimize it,

涵盖目前识别和智能计算的理论和方法，包括支持向量机，神经网络，决策树，粗糙集理论，模糊集理论，和遗传算法-Covers the recognition and intelligent computing theory and methods, including support vector machines, neural networks, decision trees, rough set theory, fuzzy set theory and genetic algorithm

对目标函数的松弛变量引入一个参数μ，优化基本的最小二乘一类支持向量机算法。同样是解决线性问题，避免了二次规划的复杂问题(A parameter min is introduced into the relaxation variable of the objective function, and the basic least squares support vector machines algorithm is optimized. It also solves the linear pr