The paper presents a fuzzy neural network whose structure accounts
for relations between input variables of the system under consideration.
This modification results in a simple fuzzy model with improved
approximation accuracy. An example of nonlinear time series prediction
using the proposed fuzzy neural network is also included
"Therefore, fuzzy models with the partition types described above do not always produce best results. A fuzzy system incorporating interactions between the input variables into the inference process was proposed in . This paper exposes the details of this system and provides a comprehensive description of its theoretical and practical aspects. "
[Show abstract][Hide abstract] ABSTRACT: 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.
IEEE Transactions on Fuzzy Systems 02/2003; 11(1):121-134. DOI:10.1109/TFUZZ.2002.803491 · 8.75 Impact Factor
"Although the use of multidimensional membership functions , ,  alleviates this drawback, it introduces the problem of exponential parameter increase and significantly reduces the model inter- pretability. An alternative scenario approximates by composition of binary relations representing linear dependencies . "
[Show abstract][Hide abstract] ABSTRACT: An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational input partition that allows for transparent modeling of linear dependencies between the inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds strongest dependencies from numerical data. An application example illustrates the usefulness of the proposed approach.
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