The state variable approach of modern control theory provides a uniform and powerful method of representing systems of arbitrary order, linear or nonlinear, with time-varying or constant coefficients.
现代控制理论的状态变量法提供了一种统一、高效的方法来描述具有任意阶次、线性或非线性、时变或常系数的各种系统。
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.
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。
Based on linear unbiased minimum variance estimation theory, a fusion algorithm which fused the state vector of nonlinear systems with dissimilar sensors with arbitrary correlated noises is developed.
基于线性无偏最小方差估计理论，提出了一种任意相关噪声异类传感器非线性系统状态矢量融合算法。
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