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.
利用支持向量机表达地基承载力与地基参数之间的非线性映射关系，在此基础上计算地基的承载力。
Support vector machine represented the nonlinear relationship between parameter of foundation and foundation bearing capacity, and compute the foundation bearing capacity based on this relationship.
支持向量机(SVM)作为一种新型的非线性建模方法，适合于处理小样本和高维数的建模问题。
Support vector machines (SVM) is a new nonlinear modeling method which is suitable for solving small samples and high dimension modeling problems.
本文基于虚拟目标值反馈调整(VRFT)方法的思想，利用支持向量机(SVM)，给出一种非线性控制器直接设计方法。
Motivated by the virtual reference feedback tuning (VRFT) method, we propose a new direct nonlinear controller design method using virtual reference (VR) and support vector machine (SVM).
提出将支持向量机网络应用于含不同浓度杂质气体的非线性荧光光谱的识别。
That the support vector machine network is applied to recognize the nonlinear fluorescence spectrum of impurities of different concentrations in air is proposed.
在软测量建模过程中，基于支持向量机的算法能较好地解决小样本、非线性、高维数、局部极小点等问题。
In model establishment of soft-sensing, the problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine algorithm.
经过实验证明支持向量机在解决小样本、非线性问题中表现出很好的优势。
Experimental results indicate that the support vector machine performs a number of unique advantages in solving the small sample size, non-linear problems.
同时针对神经网络易于陷入局部极值、结构难以确定和泛化能力较差的缺点，引入了能很好解决小样本、非线性和高维数问题的支持向量回归机来进行油气田开发指标的预测；
The method of support vector regression which can well resolve the problem with the insufficient swatch, nonlinear and high dimension is introduction to predict the development index of gas-field.
针对非线性时变的发酵过程，建立了用于产物浓度预估的支持向量机(SVM)模型。
In accordance with the features of non-linear and time varying for ferment process, a support vector machines (SVM) model is established for estimating the concentration of product.
针对非线性控制系统辨识建模较为困难的问题，利用回归型支持向量机(SVR)设计了一例控制系统的辨识建模系统。
Aiming at the problem of difficult system identification modeling for control system, an identification modeling system was designed for control system by using support vector regression (SVR).
提出了一种用支持向量机校正传感器非线性误差的原理和方法。
Principle and method of correcting nonlinear error of sensors based on the support vector machine is given.
针对土石坝渗透参数和测压管水位间复杂的非线性关系，应用最小二乘支持向量机于土石坝渗透系数的反演。
In view of complex nonlinear relationship between dam seepage parameters and piezometric tube level, the least squares support vector machine is applied to the back analysis of seepage parameters.
利用支持向量机推求管网节点水头和供水泵站供水量、供水压力的非线性关系。
The nonlinear relation between node pressure head of the pipe network and water capacity and pressure of water supply pump station was deduced by SVM.
提出了一种基于最小二乘支持向量机(LSSVM)非线性观测器的卫星姿态控制系统故障诊断方法。
A fault diagnosis method for satellite attitude control systems is presented based on least squares support vector machine (LSSVM).
针对压力传感器输出特性受温度变化和电压波动影响的问题，提出了应用支持向量机对压力传感器输出特性进行非线性补偿的校正模型。
In view of characteristics of pressure sensor affected by temperature changes and voltage fluctuation, a correction model of pressure sensor is presented based on Support Vector Machine.
通过引入核函数，支持向量机可以很容易地实现非线性算法。
SVM can deal with nonlinear problems in classification and Regression easily by using kernel functions.
考虑到电梯交通流本身所存在的非线性、复杂性和随机性，提出了一种基于小波支持向量机的电梯交通流预测模型。
Considering the nonlinearity, complexity and randomicity of elevator traffic flow, the prediction model of elevator traffic flow based on wavelet support vector machines was proposed.
为了解决支持向量机算法在多用户检测中存在的模型复杂及产生的支持向量数目较多的问题，该文提出一种新的非线性多用户检测算法。
To solve the problems of the complexity of SVM-MUD model and the number of support vectors, a new algorithm for nonlinear multiuser detection is proposed in the paper.
我们提出了使用支持向量机作为迟滞非线性建模和控制工具的具体算法，来分析和精确控制迟滞系统。
A support vector machine based modeling approach and a support vector machine based controlling approach are presented to analyze and control nonlinear systems with hysteresis.
支持向量机(SVM)是基于统计学习理论的一种智能学习方法，可以用来解决样本空间的高度非线性的模式识别等问题。
Support Vector Machine (SVM) is an intellectual learning method based on the statistics theory. The SVM can solve problems of complicated nonlinear pattern recognition of spatial samples.
支持向量机是一种基于统计理论的机器学习算法，在解决小样本、非线性及高维模式识别中有独特的优势。
Support vector machine is a kind of machine study algorithm based on statistic theory, it has special advantage in solving small sample, non-linear and high dimension mode recognition.
针对木材干燥系统强耦合非线性的特性，提出了一种基于小波最小二乘支持向量机的预测控制方法。
Aiming at the strongly coupling nonlinear characteristics of the timber desiccation system, the predictive control method based on wavelet least square support vector machine is proposed.
提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。
A modeling method for nonlinear dynamic system based on Support Vector Regression (SVR) was proposed in this paper.
提出一种基于最小二乘支持向量机(LS - SVM)的非线性系统预测控制算法。
A nonlinear predictive control algorithm based on least squares support vector machines (LS-SVM) model was proposed.
支持向量机(SVM)是一种线性机器，广泛用于模式分类和非线性回归。
The support vector machine (SVM) is a linear classification machine, it is used commonly in the pattern recognition and nonlinear regression.
该文对于未知非线性离散单输入单输出（SISO）系统提出了一种基于支持向量机的内模控制方法。
This paper presents an approximate internal model control approach for unknown nonlinear discrete SISO systems based on the support vector machine(SVM).
支持向量机方法能够解决小样本情况下非线性函数拟合的通用性和推广性的问题，是求复杂的非线性拟合函数的一种非常有效的技术。
The problems of universality and extensibility in nonlinear function approximation using small samples can be solved by the method, it a very efficient technique for nonlinear function approximation.
支持向量机（SVM）回归理论与神经网络等非线性回归理论相比具有许多独特的优点。
The support vector machines theory is shown to have excellent performance compared with other non-linear regression, such as neural networks.
支持向量机（SVM）回归理论与神经网络等非线性回归理论相比具有许多独特的优点。
The support vector machines theory is shown to have excellent performance compared with other non-linear regression, such as neural networks.
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