caa a22 4500 475800893 CHVBK 20180406123736.0 cr unu---uuuuu 170329e20001201xx s 000 0 eng 10.1023/A:1026571426761 doi (NATIONALLICENCE)springer-10.1023/A:1026571426761 Nonlinear System Identification Using Lyapunov Based Fully Tuned Dynamic RBF Networks [Elektronische Daten] [Li Yan, N. Sundararajan, P. Saratchandran] This paper presents a stable, on-line identification scheme for multivariable nonlinear dynamic system. Growing Gaussian Radial Basis Function (GRBF) network with all its parameters being adaptable is used to approximate an unknown nonlinear system. Based on a continuous-time framework, the parameter adjustment law is derived using Lyapunov synthesis approach, which guarantees the stability of the overall system. In addition, to ensure the convergence of the approximation error, a growing strategy for the network is selected and a dead zone is incorporated in the tuning law. Simulation studies on identifying a time-varying nonlinear missile dynamics illustrate the superior performance of the proposed scheme. The studies also indicate that stability and reduction in approximation error can be extended to a network with pruning strategy, thereby resulting in a Growing and Pruning (GAP) RBF network, which can implement a more compact network structure. Kluwer Academic Publishers, 2000 Growing and Pruning (GAP) nationallicence Lyapunov stability theory nationallicence neural network nationallicence nonlinear dynamic system nationallicence Radial Basis Function (RBF) network nationallicence stable identification nationallicence Yan Li School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore aut Sundararajan N. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore aut Saratchandran P. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore aut Neural Processing Letters Kluwer Academic Publishers 12/3(2000-12-01), 291-303 1370-4621 12:3<291 2000 12 11063 https://doi.org/10.1023/A:1026571426761 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1026571426761 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yan Li School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore aut NATIONALLICENCE 700 1- Sundararajan N. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore aut NATIONALLICENCE 700 1- Saratchandran P. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore aut NATIONALLICENCE 773 0- Neural Processing Letters Kluwer Academic Publishers 12/3(2000-12-01), 291-303 1370-4621 12:3<291 2000 12 11063 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer