Rule extraction, complexity reduction and evolutionary optimization for fuzzy modeling
International Journal of Knowledge-based and Intelligent Engineering Systems - Soft Computing and its Applications to E-Business
Modeling of the angle of shearing resistance of soils using soft computing systems
Expert Systems with Applications: An International Journal
A fuzzy neural network with fuzzy impact grades
Neurocomputing
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy
Expert Systems with Applications: An International Journal
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
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This paper presents a new approach to fuzzy rule-based modeling of nonlinear systems from numerical data. The novelty of the approach lies in the way of input partitioning and in the syntax of the rules. This paper introduces interpretable relational antecedents that incorporate local linear interactions between the input variables into the inference process. This modification improves the approximation quality and allows for limiting the number of rules. Additionally, the resulting linguistic description better captures the system characteristics by exposing the interactions between the input variables.