Learning of Fuzzy Cognitive Maps for simulation and knowledge discovery

Nápoles, Gonzalo; Grau, Isel; Pérez-García, Ricardo; Bello, Rafael

doi:10.2991/.2013.4

Cited by 8 publications

(6 citation statements)

References 28 publications

(34 reference statements)

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“…To mitigate this limitation, further interaction amongst concepts is captured in subsequent iterations of the map. Finally, the mapping outputs a convergence plot that ascribes a numerically weighted hierarchy to the human causal factors, determined through an inference algorithm based on population heuristic search methods [51,73,79]. The outputs of the maps (convergence plots) provide a ranking of the causal factors according to their calculated propensity as a root cause for a pilot's in-flight startled reaction.…”

Section: Fcm Resultsmentioning

confidence: 99%

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

Yusuf

Kor

Tawfik

2022

Sensors

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This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space’s characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.

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Section: Fcm Resultsmentioning

confidence: 99%

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

Yusuf

Kor

Tawfik

2022

Sensors

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“…The activation degree of a neuron plays a relevant role during the FCM interpretation: the higher the activation value of a neuron, the stronger its influence over the system [13]. Moreover, a transfer function f : R → I is used to keep the activation value of neurons in the interval I = [0, 1] or I = [−1, 1].…”

Section: Fuzzy Cognitive Mapsmentioning

confidence: 99%

Learning and Convergence of Fuzzy Cognitive Maps Used in Pattern Recognition

Nápoles

Papageorgiou

Bello

et al. 2016

Neural Process Lett

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In recent years fuzzy cognitive maps (FCM) have become an active research field due to their capability for modeling complex systems. These recurrent neural models propagate an activation vector over the causal network until the map converges to a fixed-point or a maximal number of cycles is reached. The first scenario suggests that the FCM converged, whereas the second one implies that cyclic or chaotic patterns may be produced. The nonstable configurations are mostly related with the weight matrix that defines the causal relations among concepts. Such weights could be provided by experts or automatically computed from historical data by using a learning algorithm. Nevertheless, from the best of our knowledge, population-based algorithms for FCM-based systems do not include the map convergence into their learning scheme and thus, non-stable configurations could be produced. In this research we introduce a population-based learning algorithm with convergence features for FCMbased systems used in pattern classification. This proposal is based on a heuristic procedure, called Stability based on Sigmoid Functions, which allows improving the convergence of sigmoid FCM used in pattern classification. Numerical simulations using six FCM-based classifiers have shown that the proposed learning algorithm is capable of computing accurate parameters with improved convergence features.

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“…In these situations the FCM cannot stabilize, leading to confusing system responses. During the simulation phase on a Sigmoid FCM, the activation value of each map neuron is influenced by the values of the connected concepts with the appropriate weights [15]. It shows the causal effect of changes on the neurons' activation value on the whole system.…”

Section: G Nápoles Et Al / How To Improve the Convergence On Sigmoimentioning

confidence: 99%

“…As a brief categorization, they could be gathered in three large groups [15]: Hebbian-type, population-based and hybrid methods. However, several of these learning methods cannot explicitly guarantee the system stability (e.g.…”

Section: Improving Stability On Sigmoid Fcmmentioning

confidence: 99%

How to improve the convergence on sigmoid Fuzzy Cognitive Maps?

Nápoles

Bello

Vanhoof

2014

IDA

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Fuzzy Cognitive Maps (FCM) may be defined as Recurrent Neural Networks that allow causal reasoning. According to the transformation function used for updating the activation value of concepts they can be characterized as discrete or continuous. It is remarkable that FCM having discrete neurons never exhibit chaotic states, but this premise cannot be guaranteed for FCM having continuous concepts. On the other hand, complex Sigmoid FCM resulting from experts or learning algorithms often show chaotic or cyclic patterns, therefore leading to confusing interpretation of the investigated system. The first contribution of this paper is focused on explaining why most studies on FCM stability are not applicable to FCM used on classification or decision-making tasks. Next we describe a non-direct learning methodology based on Swarm Intelligence for improving the system stability once the causal weight estimation is done. The objective here is to find a specific threshold function for each map neuron simulating an external stimulus, instead of using the same transformation function for all concepts. At the end, we can compute more stable maps, so better consistency in hidden patterns is achieved. G. Nápoles et al. / How to improve the convergence on sigmoid FCM?decision-making tasks, risk analysis, prediction, text categorization, pattern recognition, management, and classification. However, estimating parameters that characterize the whole system (e.g. the causal weight matrix) may be tedious for humans, leading to inefficient models. In order to increase the reliability of FCM-based models several learning algorithms for tuning such parameters have been introduced [10], although most of them are focused on computing causal relations between map concepts.On the other hand, existing approaches suppose that FCM are closed systems and they do not consider external influences, while other factors such as the FCM stability are frequently ignored. As far as known, there is no existence of any learning method for enhancing the system stability once the system causality is established. For example, let us suppose a FCM resulting from experts where causal connections may be partially modified (e.g. we know the direction of causalities and an approximation of their values that should be preserved). Can we expect lineal stability in the final map? If not, how to improve the stability of the system without affecting causal connections estimated by experts?In the literature a few researches concerning FCM convergence have been proposed. For instance, Kosko [4] developed an analytic method based on Liapounov functions for reaching stable solutions on Feedback Standard Additive Models (SAM -which share several of the FCM characteristics). Unfortunately, Kosko concluded that such conditions cannot be extended to FCM due to the large number of feedback links involved in FCM-based models. More recently, other analytical methods were introduced (see Section 3), but we believe that such approaches are mostly useful for stabilizing FCM used in...

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Learning of Fuzzy Cognitive Maps for simulation and knowledge discovery

Cited by 8 publications

References 28 publications

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

Learning and Convergence of Fuzzy Cognitive Maps Used in Pattern Recognition

How to improve the convergence on sigmoid Fuzzy Cognitive Maps?

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