@article{scholars8888,
          volume = {51},
            note = {cited By 3},
             doi = {10.1016/j.asoc.2016.11.015},
           title = {Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS},
            year = {2017},
         journal = {Applied Soft Computing Journal},
       publisher = {Elsevier Ltd},
           pages = {130--144},
          author = {Hassan, S. and Khanesar, M. A. and Jaafar, J. and Khosravi, A.},
            issn = {15684946},
        abstract = {Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. Moreover, the extreme learning machine does not have any design parameters such as learning rate, covariance matrix, etc. The rigorous proof of universal approximation of extreme learning machine with much milder conditions makes it a preferable choice in many different approaches. Although this algorithm is optimal for the parameters which appear linearly in the consequent part of interval type-2 fuzzy logic systems, it is not optimal for the parameters of the antecedent part as it uses random parameters. In this paper, heuristic optimization approaches such as genetic algorithm and artificial bee colony are used to optimize the parameters of the antecedent part of interval type-2 fuzzy logic systems. As these methods are global optimizers, there is less possibility that they will fall in a local minima and are suitable for the selection of the parameters of the antecedent part. A comparative analysis of the optimal parameters with the randomly and manually generated parameters is presented here using noise-free and noisy Mackey-Glass time series data sets and a real world data set. Simulation results support this idea over randomly and manually generated parameters. {\^A}{\copyright} 2016 Elsevier B.V.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007270691&doi=10.1016\%2fj.asoc.2016.11.015&partnerID=40&md5=c93a4527081bb156773e2caa36a7eb08},
        keywords = {Computer circuits; Covariance matrix; Fuzzy logic; Genetic algorithms; Global optimization; Iterative methods; Knowledge acquisition; Learning systems; Optimization; Time series analysis; Virtual reality, Antecedent parameters; Artificial bee colonies; Extreme learning machine; Gradient based algorithm; Interval type-2 fuzzy logic systems; Mackey-glass time-series datum; Optimal learning; Universal approximation, Parameter estimation}
}