Self-organizing multi-modeling: A different way to design intelligent predictors

Madani, Kurosh; Thiaw, Lamine

doi:10.1016/j.neucom.2006.06.016

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…The parametric identification is performed to evaluate the parameters of the local models. One can use either a static or a recursive methodology [6].…”

Section: Nonlinear Systems and Multi-model Approachesmentioning

confidence: 99%

E2GKpro: An evidential evolving multi-modeling approach for system behavior prediction with applications

Serir

Ramasso²,

Nectoux³

et al. 2013

Mechanical Systems and Signal Processing

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Nonlinear dynamical systems identification and behavior prediction are difficult problems encountered in many areas of industrial applications such as fault diagnosis and prognosis. In practice, the analytical description of a nonlinear system directly from observed data is a very challenging task because of the the too large number of the related parameters to be estimated. As a solution, multi-modeling approaches have lately been applied and consist in dividing the operating range of the system under study into different operating regions easier to describe by simpler functions to be combined. In order to take into consideration the uncertainty related to the available data as well as the uncertainty resulting from the nonlinearity of the system, evidence theory is of particular interest, because it permits the explicit modeling of doubt and ignorance. In the context of multi-modeling, information of doubt may be exploited to properly segment the data and take into account the uncertainty in the transitions between the operating regions. Recently, the Evidential Evolving Gustafson-Kessel algorithm (E2GK) has been proposed to ensure an online partitioning of the data into clusters that correspond to operating regions. Based on E2GK, a multi-modeling approach called E2GKpro is introduced in this paper, which dynamically performs the estimation of the local models by upgrading and modifying their parameters while data arrive. The proposed algorithm is tested on several datasets and compared to existing approaches. The results show that the use of virtual centroids in E2GKpro account for its robustness to noise and generating less operating regions while ensuring precise predictions.

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“…The parametric identification is performed to evaluate the parameters of the local models. One can use either a static or a recursive methodology [6].…”

Section: Nonlinear Systems and Multi-model Approachesmentioning

confidence: 99%

E2GKpro: An evidential evolving multi-modeling approach for system behavior prediction with applications

Serir

Ramasso²,

Nectoux³

et al. 2013

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

show abstract

“…However, it is often found that no single classifier is entirely satisfactory for a particular task, and hence the idea of combining different methods in order to improve overall performance has emerged as potentially very promising. There are some examples of systems that combine classifiers, such as in [6,21,35,38,40,41]. In [40], for instance, the authors proposed an algorithm which estimates the problem complexity, and divide an initial hard classification problem into several easier ones (divide to simplify) and builds a self organized neural tree structure.…”

Section: Ensemble Systemsmentioning

confidence: 99%

Ensembles of ARTMAP-based neural networks: an experimental study

2009

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ARTMAP-based models are neural networks which use a match-based learning procedure. The main advantage of ARTMAP-based models over error-based models, such as Multi-Layer Perceptron, is the learning time, which is considered as significantly fast. This feature is extremely important in complex systems that require the use of several models, such as ensembles or committees, since they produce robust and fast classifiers. Subsequently, some extensions of the ARTMAP model have been proposed, such as: ARTMAP-IC, RePART, among others. Aiming to add an extra contribution to ARTMAP context, this paper presents an analysis of ARTMAP-based models in ensemble systems. As a result of this analysis, two main goals are aimed, which are: to analyze the influence of the RePART model in ensemble systems and to detect any relation between diversity and accuracy in ensemble systems in order to use this relation in the design of these systems.

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“…These major points could be defeated by adding selforganizing skill to a multi-model (or to a modular architecture) leading to powerful structure, especially if local models (or local modules) are ANN based units. A number of such self-organizing multiple ANN structures have been proposed and described in [9], [10], [12] and [13]. These works could be divided into two main directions.…”

Section: Introductionmentioning

confidence: 99%

Modular and Self-Organizing Connectionist Systems: Toward Higher Level Intelligent Functions

Madani¹

2014

IJC

Self Cite

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Recent advances in “neurobiology” allowed highlighting some of key mechanisms of animal intelligence. Among them one can emphasizes brain’s “modular” structure and its “self-organizing” capabilities. The main goal of this paper is to show how these primary supplies could be exploited and combined in the frame of “soft-computing” issued techniques in order to design intelligent artificial systems emerging higher level intelligent behavior than conventional Artificial Neural Networks (ANN) based structures.

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Self-organizing multi-modeling: A different way to design intelligent predictors

Cited by 12 publications

References 17 publications

E2GKpro: An evidential evolving multi-modeling approach for system behavior prediction with applications

E2GKpro: An evidential evolving multi-modeling approach for system behavior prediction with applications

Ensembles of ARTMAP-based neural networks: an experimental study

Modular and Self-Organizing Connectionist Systems: Toward Higher Level Intelligent Functions

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