Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Handbook of software reliability engineering
Handbook of software reliability engineering
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms
IEEE Transactions on Fuzzy Systems
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
A neural-fuzzy modelling framework based on granular computing: Concepts and applications
Fuzzy Sets and Systems
Some concepts of the fuzzy multicommodity flow problem and their application in fuzzy network design
Mathematical and Computer Modelling: An International Journal
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
Online extraction of main linear trends for nonlinear time-varying processes
Information Sciences: an International Journal
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The paper is concerned with the identification of fuzzy inference systems (fuzzy models) realized with the aid of the successive tuning method using a variant identification ratio that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFC-PGA) and information granulation. The HFC-PGA is a certain multi-population version of Parallel Genetic Algorithms (PGA), which is suitable for a simultaneous optimization of both the structure of the fuzzy model as well as its parameters. The granulation of information is realized with the aid of the C-Means clustering algorithm. Information granules formed in this way become essential at further stages of the construction of the fuzzy models by forming the centers (modal values) of the fuzzy sets constituting individual rules of the inference schemes. Further optimization of the fuzzy model deals with an adjustment of a suite of parameters (such as the number of input variables to be used in the model, a collection of specific subsets of the input variables, and the number of membership functions) being used by these variables, and the order and parameters of the polynomial occurring in the conclusions of the corresponding rules. An iterative development of the fuzzy model deals with its structural as well as parametric optimization via HFC-PGA, the C-Means algorithm, and a standard least square estimation method. To evaluate the performance of the proposed model, we exploit well known and commonly used data sets such as gas furnace, Mackey-Glass time series as well as medical imaging system. A comparative analysis demonstrates that the proposed model leads to the superior performance when compared with other fuzzy models reported in the literature.