@inproceedings{scholars5067,
         journal = {MATEC Web of Conferences},
           title = {Nonlinear system identification via basis functions based time domain volterra model},
         address = {Kuala Lumpur},
       publisher = {EDP Sciences},
            note = {cited By 2; Conference of 4th International Conference on Production, Energy and Reliability, ICPER 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:106620},
            year = {2014},
             doi = {10.1051/matecconf/20141302031},
          volume = {13},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904994059&doi=10.1051\%2fmatecconf\%2f20141302031&partnerID=40&md5=bf5c5cde61e94f06adf5713ccd61d418},
        abstract = {This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA). The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement. {\^A}{\copyright} 2014 Owned by the authors, published by EDP Sciences.},
        keywords = {Functions; Genetic algorithms; Nonlinear systems; Spar platforms, Basis functions; Coherent function; Exponential basis functions; Time and frequency domains; Time domain; Volterra kernels; Volterra model, Time domain analysis},
            issn = {2261236X},
          author = {Yazid, E. and Liew, M. S. and Parman, S. and Kurian, V. J. and Ng, C. Y.}
}