Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
IEEE Transactions on Information Technology in Biomedicine
Support Vector Machine Training for Improved Hidden Markov Modeling
IEEE Transactions on Signal Processing
Brief Non-singular terminal sliding mode control of rigid manipulators
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
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To improve the control precision of robotic manipulator, fuzzy support vector machines control method for robotic manipulator was presented based on genetic algorithm and least square algorithm. Fuzzy algorithm was used to decouple joints. Using support vector machines, fuzzy logical control of complete process and treatment of non-linear signal were realized. The controller parameters were optimized by hybrid learning algorithm. First, least square algorithm was used for off-line optimization to form support vector machines control system. Then, genetic algorithm was used for on-line optimization to get the optimal performance parameters of support vector machines and the optimal fuzzy proportional parameters. The simulation results of a two-link manipulator demonstrated that the control method designed gets tracking effect with high precision.