Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule

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

doi:10.1007/978-3-540-24581-0_22

Cited by 176 publications

(95 citation statements)

References 8 publications

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“…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 Non Linear Hebbian Learning (NHL) method is a semi-automated approach that requires initial human intervention [7]. This algorithm is based on the Hebbian theory, but it uses a nonlinear extension to the basic Hebbian Rule [9] by introducing modified weight update formula.…”

Section: Fcm With Non Linear Hebbian Learningmentioning

confidence: 99%

“…Papageorgiou et al [7] proposed the Clinical Treatment Simulation Tool for Decision making in radiotherapy (CTST-FCM) model which is based on NHL algorithm. The CTST-FCM tool is efficient and useful especially for the case of complex radiotherapy treatment, as it can give information about the influence of other characteristics (such as the volume of healthy tissue or organs that can absorb radiation apart from the tumor).FCM-NHL has been also used for tumor grading tool in order to achieve tumor malignancy classification.…”

Section: Fcm With Non Linear Hebbian Learningmentioning

confidence: 99%

See 2 more Smart Citations

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

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Section: Fcm Based On Hebbianlearning Methodsmentioning

confidence: 99%

Section: Fcm With Non Linear Hebbian Learningmentioning

confidence: 99%

Section: Fcm With Non Linear Hebbian Learningmentioning

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

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“…Likewise, the tool includes 4 Hebbian-type learning methods: Active Hebbian Learning [21], Differential Hebbian Learning [22], Non-linear Hebbian Learning [23] and the Balanced Differential Algorithm proposed by Huerga [24]. We suggest using heuristic algorithms when facing pattern classification problems, since in such cases Hebbian-type methods report poor performance.…”

Section: A Estimating the Causal Weightsmentioning

confidence: 99%

A computational tool for simulation and learning of Fuzzy Cognitive Maps

Nápoles

Grau

Bello

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-During the last decade Fuzzy Cognitive Maps (FCM)have become a useful tool for solving unstructured problems. In a few words they could be defined as Recurrent Neural Networks for simulating complex systems, where neurons denote concepts, objects or entities of the investigated system. Normally FCM are entirely designed using the best knowledge of a group of experts in a given domain, so frequently learning algorithms for tuning the model parameters are required. Despite the theoretical advances in such fields, the lack of a suitable computational framework for handling FCM-based systems is still an open problem. This paper introduces a novel tool for designing and simulating FCM which gathers several learning algorithms for adjusting the introduced parameters. More specifically, the framework includes supervised and unsupervised learning algorithms for computing the causal weights, algorithms for optimizing the network topology in large FCM (without losing significant information) and also methods for improving the global convergence on continuous FCM. It should be stated that these algorithms are oriented to prediction tasks, but they could be easily extended to other fields. Index Terms -FCM, prediction problems, modeling, learning algorithms, computational framework.

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“…[6][7][8][9]) discussing the so-called nonlinear Hebbian learning (NHL). The learning rule used in this approach is the Oja rule [6], although in [7] several extensions are added to it. The procedure is as follows: An initial FCM is constructed by the experts.…”

Section: Fuzzy Cognitive Maps and Learningmentioning

confidence: 99%

Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning

Gregor

Groumpos

2013

IFIP Advances in Information and Communication Technology

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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.

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Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule

Cited by 176 publications

References 8 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 computational tool for simulation and learning of Fuzzy Cognitive Maps

Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning

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