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16 results on '"Ye, Guoqiang"'

6. Study of RBF Neural Network Based on PSO Algorithm in Nonlinear System Identification

Author

Ye Guoqiang, Li Weiguang, and Wan Hao

Subjects
Neutral network, Quantitative Biology::Neurons and Cognition, Artificial neural network, Nonlinear system identification, Computer science, business.industry, Computer Science::Neural and Evolutionary Computation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, MathematicsofComputing_NUMERICALANALYSIS, Initialization, Particle swarm optimization, Pattern recognition, Computer Science::Numerical Analysis, Nonlinear system, Probabilistic neural network, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Computational Engineering, Finance, and Science, Artificial intelligence, business, Hierarchical RBF
Abstract

Development of neural network provided new thought for nonlinear system identification. RBF neural network was widely studied in nonlinear system identification by good approximation ability and fast convergence thereof. In the paper, RBF neural network based on PSO algorithm was proposed, global searching property of PSO algorithm was utilized for remedying RBF local approximation, initial weights of RBF neural network and the base width were globally optimized, insufficiency in RBF neural network random initialization weights and base width was remedied, and identification precision of RBF neural network on nonlinear system was improved aiming at problems of RBF neutral network in nonlinear system identification application, such as local approximation and base width random initialization. The simulation results showed that RBF neural network based on PSO algorithm, proposed in the paper, had prominently better identification precision on nonlinear system than identification of RBF neural network based on GA algorithm and the traditional RBF neural network, and it had great significance on identification of nonlinear systems.

Published
2015

16. Global Stabilization of a Class of Nonminimum-Phase Nonlinear Systems by Sampled-Data Output Feedback.

Author

Lin, Wei, Wei, and Ye, Guoqiang

Subjects
FEEDBACK control systems, NONLINEAR dynamical systems, GLOBAL asymptotic stability, LYAPUNOV functions, CLOSED loop systems, DISCRETE-time systems, NONLINEAR systems
Abstract

In this technical note, global asymptotic stabilization by sampled-data output feedback is investigated for a class of nonminimum-phase nonlinear systems. Under a global Lipschitz condition imposing on both zero dynamics (i.e., the so-called driven system) and the driving system, together with a lower-triangular form, we prove that the nonminimum-phase system is globally stabilizable by a sampled-data dynamic output compensator that is composed of a nonlinear observer and a linear controller, both in discrete-time. The sampled-data feature makes the proposed output feedback controller easier for digital implementation. [ABSTRACT FROM AUTHOR]

Published
2016
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