Linguistic approximation and semantic adjustment in the modeling process
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Fuzzy Logic-A Modern Perspective
IEEE Transactions on Knowledge and Data Engineering
Hybrid fuzzy modeling of chemical processes
Fuzzy Sets and Systems - Fuzzy models
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model
Expert Systems with Applications: An International Journal
A multi-objective neuro-evolutionary algorithm to obtain interpretable fuzzy models
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Fuzzy model tuning using simulated annealing
Expert Systems with Applications: An International Journal
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Isolines of statistical information criteria for relational neuro-fuzzy system design
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Theoretical studies have shown that fuzzy models are capable of approximating any continuous function on a compact domain to any degree of accuracy. However, constructing a good fuzzy model requires finding a good tradeoff between fitting the training data and keeping the model simple. A simpler model is not only easily understood, but also less likely to overfit the training data. Even though heuristic approaches to explore such a tradeoff for fuzzy modeling have been developed, few principled approaches exist in the literature due to the lack of a well-defined optimality criterion. In this paper, we propose several information theoretic optimality criteria for fuzzy models construction by extending three statistical information criteria: 1) the Akaike information criterion [AIC] (1974); 2) the Bhansali-Downham information criterion [BDIC] (1977); and 3) the information criterion of Schwarz (1978) and Rissanen (1978) [SRIC]. We then describe a principled approach to explore the fitness-complexity tradeoff using these optimality criteria together with a fuzzy model reduction technique based on the singular value decomposition (SVD). The role of these optimality criteria in fuzzy modeling is discussed and their practical applicability is illustrated using a nonlinear system modeling example