Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems

Papakostas, George A.; Koulouriotis, Dimitrios E.; Polydoros, Athanasios; Tourassis, Vassilios D.

doi:10.1016/j.eswa.2012.02.148

Cited by 73 publications

(37 citation statements)

References 21 publications

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“…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

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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.

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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

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“…Fuzzy cognitive map approach, which is a soft computing method that provides a powerful and flexible framework for knowledge representation and reasoning, is a convenient tool for dynamic system modeling [23]. Several researchers have recently made progress in the areas of classification [24] and emotional prediction [25] with FCM-based models. These models involve updating the node state values and the causal relationships between concepts to simulate dynamic behavior.…”

Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

Zhang

Sun

2017

Mathematical Problems in Engineering

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Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps). Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%-20% better than those of single features. The proposed FCM learning algorithm generates 5%-20% performance improvement compared with traditional classification networks.

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“…The precise way in which this concept should be represented mathematically is still a matter of debate in the literature. One of the simplest ways of proceeding is the delta-formulation of the Hebbian Learning Rule [3,18], and we follow this approach also taken elsewhere [16,19].…”

Section: Introductionmentioning

confidence: 99%

Synchronization in phase-coupled Kuramoto oscillator networks with axonal delay and synaptic plasticity

Timms

English

2014

Phys. Rev. E

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We explore both analytically and numerically an ensemble of coupled phase-oscillators governed by a Kuramoto-type system of differential equations. However, we have included the effects of time-delay (due to finite signal-propagation speeds) and network plasticity (via dynamic coupling constants) inspired by the Hebbian learning rule in neuroscience. When time-delay and learning effects combine, novel synchronization phenomena are observed. We investigate the formation of spatio-temporal patterns in both one-and two-dimensional oscillator lattices with periodic boundary conditions and comment on the role of dimensionality.

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Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems

Cited by 73 publications

References 21 publications

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

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

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

Synchronization in phase-coupled Kuramoto oscillator networks with axonal delay and synaptic plasticity

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