Structure identification of fuzzy model
Fuzzy Sets and Systems
Applying genetics to fuzzy logic
AI Expert
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Hierarchical genetic fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
System Identification using Structured Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
Soft computing in engineering design - A review
Advanced Engineering Informatics
Engineering Applications of Artificial Intelligence
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Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for modeling of the explosive cutting process of plates by shaped charges. The aim of such modeling is to show how the depth of penetration varies with the variation of important parameters, namely, the apex angle, standoff, liner thickness, and mass of charge. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by a GA.