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  “tsk 3 fuzzy model”译为未确定词的双语例句
    Method of learning TSK fuzzy model by cooperative coevolution
    TSK模糊模型的协同进化学习方法
短句来源
    Identification and Control for TSK Fuzzy Model Based on SVM
    基于支持向量机的TSK模糊模型辨识与控制
短句来源
    Based on the support vector machine (SVM) identification and the control for TSK fuzzy model of the nonlinear system are studied. Linear control theory can be used to control nonlinear system using the TSK model.
    研究非线性系统TSK模糊模型的辨识与控制,利用TSK模型,可以将线性控制理论应用于非线性系统控制。
短句来源
    The TSK fuzzy model is identified based on the SVM and the recursive least square method, and membership function parameters are optimized with the genetic algorithm so that identification error is minimized.
    基于支持向量机和递推最小二乘法,辨识出TSK模糊模型,并且通过遗传算法优化隶属度函数参数,最小化辨识误差。
短句来源
    TSK fuzzy controller based on TSK fuzzy model with nonlinear controller assures the stability of closed loop system.
    所设计的TSK模糊控制器是一个基于TSK模糊模型的非线性控制器,能保证闭环控制系统的稳定性。
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To the problem of multi-constraint and multi-target optimization in learning fuzzy model, TSK fuzzy model is decomposed of twain different species. The method of learning the model by cooperative coevolution is proposed. Some problems related to each species coding and different evolution computing means, individual cooperation and fitness evaluation strategy, consequent parameters estimation, are discussed. The characteristic of the method requests a little of previous information about...

To the problem of multi-constraint and multi-target optimization in learning fuzzy model, TSK fuzzy model is decomposed of twain different species. The method of learning the model by cooperative coevolution is proposed. Some problems related to each species coding and different evolution computing means, individual cooperation and fitness evaluation strategy, consequent parameters estimation, are discussed. The characteristic of the method requests a little of previous information about objects, and is able to obtain compact fuzzy model. An example of function approximation shows the validity of the method.

针对TSK模糊模型的学习是多约束和多目标优化问题,提出将TSK模糊模型分解为两类不同的种群,协作共同进化的模型学习方法.论述了所涉及的相关问题,包括各种群的编码及其不同的进化计算,各种群个体的合作及其适应值评估策略,模型的后件参数估计方法.该方法要求先验知识少,收敛速度快,能形成简洁的模糊模型,最后以函数近似为例说明了该方法的有效性.

Based on the support vector machine (SVM) identification and the control for TSK fuzzy model of the nonlinear system are studied. Linear control theory can be used to control nonlinear system using the TSK model. The TSK fuzzy model is identified based on the SVM and the recursive least square method, and membership function parameters are optimized with the genetic algorithm so that identification error is minimized. The controller includes two parts for TSK model: feedback...

Based on the support vector machine (SVM) identification and the control for TSK fuzzy model of the nonlinear system are studied. Linear control theory can be used to control nonlinear system using the TSK model. The TSK fuzzy model is identified based on the SVM and the recursive least square method, and membership function parameters are optimized with the genetic algorithm so that identification error is minimized. The controller includes two parts for TSK model: feedback control for subsystem with the maximum weight and its supervised control for guaranting the system stability. Simulation results of the identification and the control show the effectiveness of the method.

研究非线性系统TSK模糊模型的辨识与控制,利用TSK模型,可以将线性控制理论应用于非线性系统控制。基于支持向量机和递推最小二乘法,辨识出TSK模糊模型,并且通过遗传算法优化隶属度函数参数,最小化辨识误差。针对TSK模型进行控制,控制器包括两个部分:权重最大子系统反馈控制及其监督控制,监督控制保证了系统的稳定性。辨识和控制仿真结果证明了算法的有效性。

A new hybrid learning algorithm was proposed to train the fuzzy neural network based on TSK fuzzy model. Firstly, fuzzy c-means algorithm was applied to initialize the parameters of the fuzzy neural network. Secondly, the parameters of the premise part of the fuzzy rule were learned by the gradient descent algorithm. Finally, the parameters of consequent part were learned by the partial least squares algorithm. The proposed hybrid method could automatically give appropriate initial...

A new hybrid learning algorithm was proposed to train the fuzzy neural network based on TSK fuzzy model. Firstly, fuzzy c-means algorithm was applied to initialize the parameters of the fuzzy neural network. Secondly, the parameters of the premise part of the fuzzy rule were learned by the gradient descent algorithm. Finally, the parameters of consequent part were learned by the partial least squares algorithm. The proposed hybrid method could automatically give appropriate initial parameters of the fuzzy neural network and prevent the fuzzy rule number from increasing for high-dimensional systems. The results of simulation and industrial application show that the hybrid learning algorithm has properties of fast convergence and high accuracy. The proposed method has been applied to build a soft-sensor for measuring the 4-CBA concentration in the industrial PTA(Purified Terephthalic Acid)oxidation process and the result demonstrates that the proposed method is suitable to practical application.

提出了一阶TSK模糊神经网络的混合学习算法,算法由三部分组成:基于模糊聚类的网络初始化;基于梯度下降的规则前件的学习算法;基于部分最小二乘的规则后件的学习算法。该混合算法可以根据训练样本的分布自动确定模糊神经网络的初始值,当输入变量个数多时不会出现模糊规则数爆炸现象,训练速度快,模型精度高。将混合学习算法应用到PTA工业过程中4-CBA含量的软测量建模中,取得了令人满意的效果。

 
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