An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators

被引：0
作者：
Amitava Chatterjee
Keigo Watanabe
机构：
[1] Saga University,Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering
来源：
Neural Computing & Applications | 2006年 / 15卷
关键词：
Neuro-fuzzy systems; Particle swarm optimization; Robot manipulators;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The present paper describes the development of a Takagi-Sugeno (TS)-type Neuro-fuzzy system (NFS) for dynamic modeling of robot manipulators. The NFS has been trained by a relatively new combinatorial metaheuristic optimization method, called particle swarm optimization (PSO). The development of such an intelligent, robust, dynamic models for robot manipulators can immensely help in deriving proper position/velocity control strategies in offline situations with these accurately developed models. The proposed PSO-based NFS has been successfully applied to two-link and three-link model robot manipulators.
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页码：55 / 61
页数：6
相关论文
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