Active Hebbian learning algorithm to train fuzzy cognitive maps

Papageorgiou, Elpiniki I.; Stylios, Chrysostomos; Groumpos, Peter P.

doi:10.1016/j.ijar.2004.01.001

Cited by 297 publications

(117 citation statements)

References 16 publications

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…However, BDA works only on binary FCMs. To overcome the limitations of using the existing learning methods from Neural Networks domains into FCMs, two methods have been proposed: the non Linear Hebbian Learning [7] and Active Hebbian Learning algorithms [8].…”

Section: Fcm Based On Hebbianlearning Methodsmentioning

confidence: 99%

“…The AHL algorithm takes into consideration the experts' knowledge and experience for the initial values of the weights, which are derived from the summation of experts' opinions [8]. Besides, it supposes that there is a sequence of activation concepts, which depends on the specific problem's configuration and characteristics.…”

Section: Fcm With Active Hebbian Learning (Ahl)mentioning

confidence: 99%

See 1 more Smart Citation

A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

Bourgani¹,

Stylios²,

Georgopoulos³

et al. 2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

View full text Add to dashboard Cite

Section: Fcm Based On Hebbianlearning Methodsmentioning

confidence: 99%

Section: Fcm With Active Hebbian Learning (Ahl)mentioning

confidence: 99%

A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

Bourgani¹,

Stylios²,

Georgopoulos³

et al. 2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

View full text Add to dashboard Cite

“…There are two classes of FCM learning algorithms: Hebbian-based learning and evolved-based learning. The former are Hebbian-based algorithms [17,44,45], mainly including NHL (nonlinear Hebbian learning) and AHL (active Hebbian learning). The latter are learning algorithms based on evolution theory [17,46,47], which are composed of PSO (particle swarm optimization), RCGA (real coded genetic algorithm), etc.…”

Section: Proposed Problemsmentioning

confidence: 99%

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

Peng

2017

Sustainability

View full text Add to dashboard Cite

Abstract:Haze-fog has seriously hindered the sustainable development of the ecological environment and caused great harm to the physical and mental health of residents in China. Therefore, it is important to probe the formation of haze-fog for its early warning and prevention. The formation of haze-fog is, in fact, a fuzzy nonlinear process. The formation of haze-fog is such a complex process that it is difficult to simulate its dynamic evolution using traditional methods, mainly because of the lack of their consideration of the nonlinear relationships. It is, therefore, essential to explore new perspectives on the formation of haze-fog. In this work, previous research on haze-fog formation is summarized first. Second, a new perspective is proposed on the application of fuzzy cognitive map to the formation of haze-fog. Third, a data mining method based on the genetic algorithm is used to discover the causality values of a fuzzy cognitive map (FCM) for haze-fog formation. Finally, simulation results are obtained through an experiment using the fuzzy cognitive map and its data mining method for the formation of haze-fog. The validity of this approach is determined by definition of a simple rule and the Kappa values. Thus, this research not only provides a new idea using FCM modeling the formation of haze-fog, but also uses an effective method of FCM for solving the nonlinear dynamics of the haze-fog formation.

show abstract

“…In [5] the authors propose the so-called active Hebbian learning (AHL) method, which introduces the idea of the sequence of activation. The expert specifies the sequence in which the concepts are activated.…”

Section: Fuzzy Cognitive Maps and Learningmentioning

confidence: 99%

“…The window size is understood to be the number of samples the window contains, and the overlap size is the number of samples which the window shares with the following one. Thus, windowing of w [5,4] refers to windowing with window size of 5 samples and overlap of 4 samples.…”

Section: Every-step Delta Rule With Windowed Bpttmentioning

confidence: 99%

Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning

Gregor

Groumpos

2013

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. The paper considers a novel approach to learning the weight matrix of a fuzzy cognitive map. An overview of the state-of-the-art learning methods is presented with a specific emphasis on methods initially developed for artificial neural networks, and later adapted for FCMs. These have mostly been based on the concept of Hebbian learning. Inspired by the amount of success these methods have faced in the past, the paper proposes a new approach based on the application of the delta rule and the principle of backpropagation, both of which were originally designed for artificial neural networks as well. It is shown by simulation experiments and comparison with the existing approach based on nonlinear Hebbian learning that the proposed approach achieves favourable results, and that these are superior to those of the existing method by several orders of magnitude. Finally, some possible lines of further investigation are suggested.

show abstract

Active Hebbian learning algorithm to train fuzzy cognitive maps

Cited by 297 publications

References 16 publications

A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning

Contact Info

Product

Resources

About