神经网络是非线性系统建模的重要方法。
Neural Networks is an important method in nonlinear system modeling.
将混沌BP算法应用于非线性系统建模，以求获得全局意义下的最优逼近。
The chaotic BP algorithm is applied to nonlinear system modeling to obtain the optimal approximation in the global sense.
结果表明，基于结构的神经网络建模方法对复杂非线性系统建模是有效的。
The result shows that the modeling method using architecture based neural networks is suitable to the modeling of complex nonlinear system.
首先，采用模糊T-S模型来对非线性系统建模，由线性矩阵不等式得到模糊模型的控制律。
The fuzzy T-S model is used to approximate the nonlinear systems, and the fuzzy control law of the fuzzy model is derived from the linear matrix inequality.
通过实验比较 ，径向基函数网络较反向传播算法网络更适合于肌电信号这类非线性系统建模。
Through comparing with BP networks performance, we found that RBF networks achieve better result in and are suitable for modeling a non-linear system such as EMG.
支持向量机是一种基于统计学习理论的新型机器学习方法，它可以被广泛地用于非线性系统建模。
Support vector machine (SVM) is a brand-new machine learning technique based on statistical learning theory. It is an ideal facility for modeling of various nonlinear systems.
使用建立在线性或本质线性系统基础上的传统辨识方法对各种非线性系统建模、辨识难以获得理想结果。
Perfect results of nonlinear systems identification used traditional methods established on the linear or intrinsically linear system are difficult to get.
前馈神经网络由于具有理论上逼近任意非线性连续映射的能力，因而非常适合于非线性系统建模及构成自适应控制。
Because the feedforward neural network has an ability of approach to arbitrary nonlinear mapping, it can be used effectively in the modeling and controlling of nonlinear system.
提出了模糊神经网络用于SARS疾病疫情非线性系统建模和预报的思想，该方法可以推广到各种流行性疾病的预防和控制中。
Put forward one idea of fuzzy neural networks using to distinguish and forecast non-linearity systems of SARS epidemic situation. The method can generalize to defend and control on apiece epidemic.
SVM由于其良好的泛化能力和全局最优性能，在模式识别、数据挖掘、非线性系统建模和控制等领域中展现出广泛的应用前景。
SVM has been applied to many fields such as pattern recognition, data mining, modeling and control of nonlinear system due to good generalization ability and globally optimal performance.
SVM由于其良好的泛化能力和全局最优性能，在模式识别、数据挖掘、非线性系统建模和控制等领域中展现出广泛的应用前景。
SVM has been applied to many fields such as pattern recognition, data mining, modeling and control of nonlinear system due to good generalization ability and globally optimal performance.
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