Incremental learning in Fuzzy Pattern Matching

Mouchaweh, Moamar Sayed; Devillez, Arnaud; Lecolier, G. Villermain; Billaudel, Patrice

doi:10.1016/s0165-0114(02)00060-x

Cited by 38 publications

(17 citation statements)

References 5 publications

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“…2, M is the brake torque, r 1 and r 2 are the wheels radii, F n is the normal force that the upper wheel pushes upon the lower wheel, () is the friction coefficient, 1 x  and 2 x  are angular accelerations of the wheels, u is the control signal applied to the actuator, namely the direct current (DC) motor which drives the upper wheel, and the actuator's nonlinear model is reflected in the nonlinear map b(u). Longitudinal slip  is defined as:…”

Section: Modeling the Longitudinal Slip In The Anti-lock Braking Systemsmentioning

confidence: 99%

“…The main property of the evolving Takagi-Sugeno-Kang fuzzy models, which gives them advantages over other fuzzy ones, consists in computing the rule bases by a learning process, that is, by continuous online rule base learning as shown in the classical and recent papers exemplified by [1][2][3][4][5][6][7][8][9][10]. The Takagi-Sugeno-Kang fuzzy models are obtained by evolving the model structure and parameters in terms of online identification algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

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Abstract. This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Antilock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously

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Section: Modeling the Longitudinal Slip In The Anti-lock Braking Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

View full text Add to dashboard Cite

show abstract

“…The latter is estimated based on the use of histograms. In [13], an algorithm is proposed to update recursively the conditional PDF after the classification of each point.…”

Section: Membership Function Generationmentioning

confidence: 99%

Learning in dynamic environments: application to the diagnosis of evolving systems

Mouchaweh¹,

Ayad²,

Malki³

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In dynamic environments, data characteristics may drift over time. This leads to deteriorate dramatically the performance of incremental learning algorithms over time. This is because of the use of data which is no more consistent with the characteristics of new incoming one. In this paper, an approach for learning in dynamic environments is proposed. This approach integrates a mechanism to use only the recent and useful patterns to update the classifier without a "catastrophic forgetting". This approach is used for the acoustic leak detection in the steam generator unit of the nuclear power generator "Prototype Fast React".

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“…It permits to show the limits of three incremental classification methods in non-stationary environments. These methods are IFPM (Sayed-Mouchaweh et al 2002), incremental support vector machines (ISVM) (Cauwenberghs and Poggio 2001) and incremental KNN (IKNN) (Zhou 2001). The goal is to stress the interests of the use of dynamic classification methods.…”

Section: Illustrative Examplementioning

confidence: 99%

A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems

2010

Self Cite

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Data issued of most real-world applications are evolving; they change constantly over time. In such applications, it is difficult to induce correctly a model (classifier) using traditional classification methods. Thus, it is important to use an adapted classification method to build a classifier and to update its parameters as new data is available. In this paper, we propose an adaptive classification approach based on the Fuzzy K-Nearest Neighbours (FKNN) method to monitor online evolving systems. The developed method, named semi-supervised Dynamic FKNN, comprises the following phases. In the first phase (detection phase), a class evolution can be detected and confirmed after the classification of each new pattern. Then in the second phase (adaptation phase), the parameters of the evolved class are updated incrementally. In the last phase (validation phase) the adapted classes are validated in order to keep only the representative ones. This approach is illustrated using an example of system which switches between several functioning modes.

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Incremental learning in Fuzzy Pattern Matching

Cited by 38 publications

References 5 publications

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Learning in dynamic environments: application to the diagnosis of evolving systems

A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems

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