id: 05693174
dt: j
an: 05693174
au: Li, Wei; Yang, Yupu
ti: Hybrid kernel learning via genetic optimization for TS fuzzy system
    identification.
so: Int. J. Adapt. Control Signal Process. 24, No. 1, 12-19 (2010).
py: 2010
pu: John Wiley \& Sons, Chichester
la: EN
cc: 
ut: fuzzy system identification; hybrid kernel function; support vector
    regression; genetic algorithm
ci: 
li: doi:10.1002/acs.1089
ab: Summary: This paper presents a new TS fuzzy system identification approach
    based on hybrid kernel learning and an improved Genetic Algorithm (GA).
    Structure identification is achieved by using Support Vector Regression
    (SVR), in which a hybrid kernel function is adopted to improve
    regression performance. For multiple-parameter selection of SVR, the
    proposed GA is adopted to speed up the search process and guarantee the
    least number of support vectors. As a result, a concise model structure
    can be determined by these obtained support vectors. Then, the premise
    parameters of fuzzy rules can be extracted from results of SVR, and the
    consequent parameters can be optimized by the least-square method.
    Simulation results show that the resulting fuzzy model not only
    achieves satisfactory accuracy, but also takes on good generalization
    capability.
rv: