TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy

Herrera, José Benavente; Pomares, H.; Rojas, Ignacio; Valenzuela, Olga; Prieto, Alberto

doi:10.1016/j.fss.2005.01.012

Cited by 58 publications

(28 citation statements)

References 30 publications

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“…Although they are powerful tools regarding accuracy, they present the great drawback of the lack of interpretability. Consequently, they are not so suitable for a wide range of problems where interpretability of the model is important [15]. Interpretability refers to the capability of the fuzzy model to express the behavior of the system in an understandable way [42].…”

Section: Tsk Fuzzy Rule Based System: a New Structurementioning

confidence: 99%

“…It can be in the form of any function of the input variables where the absence of some input variables is allowed. In this way, the expressive power that each rule can provide is increased and therefore a more accurate system can be developed [15]. Furthermore, the dimension difference between antecedents and consequents leads to a system having a smaller number of rules and a more compact rule base [45].…”

Section: Tsk Fuzzy Rule Based System: a New Structurementioning

confidence: 99%

“…In this paper, a TSK (Takagi-Sugeno-Kang) type fuzzy rule based system will be developed, because TSK systems have shown a powerful representative capability. They are able to describe nonlinear mappings using a small number of simple rules [15] especially in comparison with Mamdani type systems [16]. Moreover, TSK systems are very suitable for modeling dynamic systems in comparison with other fuzzy modeling methods [17].…”

Section: Introductionmentioning

confidence: 99%

“…Although the TSK systems are very powerful in designing an accurate and precise system, they suffer from interpretability issues [15,35]. There are some studies that try to improve the interpretability of the TSK systems maintaining its accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi

Esfahanipour

Zarandi

2015

Applied Soft Computing

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Section: Tsk Fuzzy Rule Based System: a New Structurementioning

confidence: 99%

Section: Tsk Fuzzy Rule Based System: a New Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi

Esfahanipour

Zarandi

2015

Applied Soft Computing

View full text Add to dashboard Cite

“…These EAs are population-based algorithms which may explore different portions of the Pareto front simultaneously. As a result, multi-objective optimization (MOO) techniques have been applied to design fuzzy systems exhibiting high accuracy and significant interpretability [19,20]. Nevertheless, when dealing with the IG-based fuzzy model, previous studies lack an optimization vehicle which considers not only the solution space being explored but also the techniques of MOO.…”

Section: Introductionmentioning

confidence: 99%

Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation

Huang¹,

Oh²,

Ding³

et al. 2011

Journal of Electrical Engineering and Technology

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-We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

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A nonlinear partial least squares algorithm using quadratic fuzzy inference system

Abdel-Rahman

Lim

2009

Journal of Chemometrics

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We introduce a new nonlinear partial least squares algorithm 'Quadratic Fuzzy PLS (QFPLS)' that combines the outer linear Partial Least Squares (PLS) framework and the Takagi-Sugeno-Kang (TSK) fuzzy inference system. The inner relation between the input and the output PLS score vectors is modeled by a quadratic TSK fuzzy inference system. The performance of the proposed QFPLS method is tested and compared against four other well-known partial least squares methods (Linear PLS (LPLS), Quadratic PLS (QPLS), Linear Fuzzy PLS (LFPLS), and Neural Network PLS (NNPLS)) on various different types of randomly generated test data. QFPLS outperformed competitors based on two comparison measures: the output variables cumulative per cent variance captured by the PLS latent variables and the root mean-square error of prediction (RMSEP).

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TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy

Cited by 58 publications

References 30 publications

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

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