A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects

Cpałka, Krzysztof; Łapa, Krystian; Przybył, Andrzej; Zalasiński, Marcin

doi:10.1016/j.neucom.2013.12.031

Cited by 80 publications

(17 citation statements)

References 40 publications

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“…Interpretability of fuzzy models can be provided in many ways, but restrictions on the learning process are imposed most commonly (see, e.g., Lughofer, 2013;Cpałka et al, 2014;Shukla and Tripathi, 2013;Ishibashi and Lucio Nascimento, Jr., 2013).…”

Section: 2mentioning

confidence: 99%

“…In literature we can find many methods to design a structure and select parameters of neuro-fuzzy systems (see, e.g., Kim et al, 2006;Angelov and Filev, 2004;Medasani et al, 1998;Rutkowski and Cpałka, 2005;Starczewski et al, 2010;Malchiodi and Pedrycz, 2013;Cpałka, 2009a;2009b;Cpałka et al, 2014;2013). In this paper we used the (λ + μ) evolutionary strategy, which belongs to the group of population based algorithms.…”

Section: Design Of Neuro-fuzzy Systems For Nonlinear Systems Modellinmentioning

confidence: 99%

“…Rule base selection, i.e., to adjust the number and the form of fuzzy rules (employed inputs and fuzzy sets occurring in the antecedents and consequents of the rules). In this case it is assumed that the shape and parameters of fuzzy sets are predefined and unchanged during the selection process (Ishibuchi and Yamamoto, 2004;Cordón et al, 2001;Cpałka et al, 2014).…”

Section: Design Of Neuro-fuzzy Systems For Nonlinear Systems Modellinmentioning

confidence: 99%

“…The key aspect of the design of a fuzzy system is to determine its parameters, including fuzzy sets present in fuzzy rules. In the literature we can find many approaches that allow us to accomplish this task, among others, gradient methods (Medasani et al, 1998;Rutkowski and Cpałka, 2005), clustering methods (Starczewski et al, 2010;Malchiodi and Pedrycz, 2013), or population based algorithms (Cpałka, 2009a;2009b;Cpałka et al, 2014;2013). The latter perform very well in practice because in addition to the shape and the position of the membership function, they also allow us to determine the form of fuzzy rules and a convenient implementation of interpretability criteria.…”

Section: Introductionmentioning

confidence: 99%

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A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Bartczuk

Przybył

Cpałka

2016

International Journal of Applied Mathematics and Computer Science

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For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system's dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules.

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Section: 2mentioning

confidence: 99%

Section: Design Of Neuro-fuzzy Systems For Nonlinear Systems Modellinmentioning

confidence: 99%

Section: Design Of Neuro-fuzzy Systems For Nonlinear Systems Modellinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Bartczuk

Przybył

Cpałka

2016

International Journal of Applied Mathematics and Computer Science

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“…We use flexible neuro-fuzzy system of the Mamdani type (see e.g. [6,7,5,8]). This system is based on the rules in the form if-then.…”

Section: Determination Of Classifiermentioning

confidence: 99%

New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values

Zalasiński

Cpałka

Hayashi

2015

Artificial Intelligence and Soft Computing

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Abstract. Identity verification based on the dynamic signature is an important issue of biometrics. There are many effective methods to the signature verification which take into account the dynamic characteristics of the signature (e.g. velocity of the pen, the pen's pressure on the surface of the graphic tablet, etc.). Among these methods, the ones based on the so-called global features are very important. In our previous paper we have proposed new algorithm for evolutionary selection of the dynamic signature global features, which selects a subset of features individually for each user. Algorithm proposed in this paper is a faster version of the method proposed earlier. During development of the algorithm we resigned from using evolutionary selection of global features and standardized working of the classifier in the context of all users. The paper contains the simulation results for the BioSecure database of the dynamic signatures.

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Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

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A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects

Cited by 80 publications

References 40 publications

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

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