Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

“…In [19], a multivariable Gaussian evolving fuzzy modeling system (eMG) is developed using a recursive clustering algorithm inspired by the idea of participatory learning. A evolving fuzzy model (eFuMo) method [20] is proposed for a monitoring system based on the normalized Mahalanobis distance. To create a new cluster, certain previous samples must satisfy the distance adding criterion in order to reduce influence of the outliers.…”

Section: Introductionmentioning

confidence: 99%

Correntropy-Based Evolving Fuzzy Neural System

Bao

Rong

Angelov

et al. 2018

IEEE Trans. Fuzzy Syst.

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Abstract-In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used meansquare error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion where the correntropy is used as the cost function so as to improve the nonGaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: i) Gaussian noise; and ii) non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other evolving fuzzy neural systems, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability.

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“…In 1979 Donald E. Gustafson and William C. Kessel published the article [9] presenting a modified Fuzzy C-Means traditional algorithm [5]. Nowadays this algorithm has become known as Gustafson-Kessel (GK), being the basis of a family of fuzzy clustering algorithms as described in [4,7,11,14,16,18].…”

Section: Introductionmentioning

confidence: 99%

Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model

Bertone

Martins

Yamanaka

2018

Tend. Mat. Apl. Comput.

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ABSTRACT.A fuzzy identification of the system's dynamic is developed with a data generated by a hydrogen fuel cell simulator. The data obtained is single input/single output, without having previous knowledge of the system model, and showing nonlinear behavior. The choice of the fuzzy method for identification is based on those particular data features, and the malleability of the mathematical fuzzy technique. The objective of the fuzzy identification is to reach an analytic formula for a better understanding of the causeeffect relationships of the data, followed by its validation. The dynamic system identification process is performed using fuzzy clustering through the Gustafson and Kessel algorithm, followed by a Takagi and Sugeno fuzzy inference method. The k-fold technique, is the cross validation tool, used to confirm the lack of data over-training. The novelty of this approach covers mathematical and engineering features that makes this study interdisciplinary. For the mathematical contribution, there is a three-dimensional graphic interpretation of the data clustering geometry, obtained through own code computer simulations. Concerning to the engineering context, the novelty is based on the use of the fuzzy approach to the hydrogen fuel cell. Both contributions have no precedent in the literature. The results of the fuzzy identification show high reliability in terms of cross validation, making the fuzzy approach a promising tool for black-box identification. Combining this technique with others will provide powerful instrument for industrial problems.

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Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Cited by 134 publications

References 58 publications

ProOpter: An advanced platform for production analysis and optimization

ProOpter: An advanced platform for production analysis and optimization

Correntropy-Based Evolving Fuzzy Neural System

Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model

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