An effective approach is developed to establish affine Takagi-Sugeno (T-S) fuzzymodel for a given nonlinear system from its input-output data. Firstly, the fuzzyc-regressionmodel (FcRM) clustering technique is appl...
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An effective approach is developed to establish affine Takagi-Sugeno (T-S) fuzzymodel for a given nonlinear system from its input-output data. Firstly, the fuzzyc-regressionmodel (FcRM) clustering technique is applied to partition the product space of the given input-output data into hyper-plan-shaped clusters. Each cluster is essentially a basis of the fuzzy rule that describes the system behaviour, and the number of clusters is just the number of fuzzy rules. Particularly, a novel cluster validity criterion for FcRM is set up to choose the appropriate number of clusters (rules). Once the number of clusters is determined, the consequent parameters of each IF-THEN rule are directly obtained from the functional cluster representatives (affine linear functions). The antecedent fuzzy sets of each IF-THEN fuzzy rule are acquired by projecting the fuzzy partitions matrix U onto the axes of individual antecedent variable to obtain point-wise defined fuzzy sets and to approximate these point-wise defined fuzzy sets by normal bell-shaped membership functions. Additionally, a check and repartition algorithm is suggested to prevent the inappropriate premise structure where separate regions of data shared the same regressionmodel. Finally, the gradient descent algorithm is included to adjust the fuzzymodel precisely. An affine T-S fuzzymodel with compact IF-THEN rules could thus be generated systematically. Several simulation examples are provided to demonstrate the accuracy and effectiveness of the affine T-S fuzzymodelling algorithm.
Dynamicmodels of maneuvering targets in nonlinear systems are usually difficult to be modeled, and inaccurate dynamicmodel will lead to the poor performance of tracking algorithm. For these problems, in this paper, ...
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ISBN:
(纸本)9780996452786
Dynamicmodels of maneuvering targets in nonlinear systems are usually difficult to be modeled, and inaccurate dynamicmodel will lead to the poor performance of tracking algorithm. For these problems, in this paper, a novel interacting Takagi-Sugeno (T-S) fuzzy multiple model maneuvering target tracking algorithm by using UKF for parameter identification is proposed (ITS-UKF). The ITS-UKF algorithm uses multiple semanticfuzzy sets to represent the target feature information, and construct a general T-S fuzzy semantic multiple model framework In the T-S fuzzy semantic multiple model framework, the intersection degree between fuzzy sets is used to estimate the transition probabilities between different fuzzy rules;fuzzy c-regression model clustering (FcRM) is used to adaptively identify the premise parameters. Moreover, the UKF is also used to identify the consequent parameters to improve the performance for nonlinear system. Simulation results show that the performance of ITS-UKF is superior to IMM-EKF (interacting multiple model extended Kalman filter), IMM-UKF (interacting multiple model unscented Kalman filter), particularly, when the target is maneuvered or the model of the target is inaccurate, the ITS-UKF algorithm has better performance
model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. I...
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model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. In this paper, an effective fuzzymodel identification algorithm for mechanical system design is developed. First, a fuzzy c-regression model clustering algorithm, in which hyperplane-shaped cluster representatives are utilized to provide a mathematical tool to partition the input-output space reasonably, is introduced. Then, an enhanced cluster validity criterion, in which the structural information hidden in the clusters can be reflected in the index, is proposed to choose the optimal number of clusters. In the proposed architecture, an improved Takagi-Sugeno fuzzymodel is proposed to describe the system. Two illustrative examples under various conditions are provided, and their performances are indicated in comparison with other published works. In comparison to these fuzzy works, the proposed fuzzymodel identification requires fewer fuzzy rules and a shorter tuning time. [DOI: 10.1115/1.4004483]
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