Fuzzy cognitive state map vs markovian modeling of user's web behavior

Meghabghab, George

doi:10.1109/icsmc.2001.973077

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…Introducing indices such as centrality and hierarchal degree (kosko 1986; Ö zesmi and Ö zesmi 2004) as well as static and dynamic analysis to forecast the system behaviour (Vasantha and Ram Kishore 1999;Meghabghab 2002;Lee and Chung 2006), incorporating time factor in FCM (Park and Kim 1995), applying combined technique of FCM (Kosko 1988;Taber 1991;Hossain and Brooks 2008) and using FCM in group decisions (Khan and Quaddus 2004) are but some of the advances made in the field of FCM.…”

Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

“…FCMs are used in various areas of application such as information retrieval, (Montazemi and Conrath 1986), medical research (Vasantha and Ram Kishore 1999), information systems development (Kardaras and Karakostas 1999), software engineering (Hossain and Brooks 2008), web design (Lee and Chung 2006), and forecasting staff behaviour on web (Meghabghab 2002), as well.…”

Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

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The Application of Fuzzy Cognitive Map in Soft System Methodology

Hanafizadeh

Aliehyaei

2011

Syst Pract Action Res

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Facing the issues of structural complexity, on which stakeholders have different views, has increasingly led to the use of Soft Systems Methodology (SSM) in solving managerial problems. Moreover, the weaknesses of this methodology in considering all point of views and ensuring the effectiveness of the proposed changes have provided the motivation for applying Fuzzy Cognitive Map (FCM) in SSM. Using FCM as a modeling tool makes it possible to combine the views of different experts and form group FCM (GFCM). GFCM has the potential to be applied as a useful decision support tool in the stage of offering recommendations and changes. The methodology proposed in this article is applied to ticketing system of Raja passenger train company. This system, influenced by various policies and views, is analyzed with the recommended methodology and then the solutions for developing the system are suggested in a prioritized manner.

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Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

The Application of Fuzzy Cognitive Map in Soft System Methodology

Hanafizadeh

Aliehyaei

2011

Syst Pract Action Res

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“…Compared with the traditional expert systems and neural networks, FCMs have several desirable properties [6], [23], such as abstraction, flexibility, adaptability, and fuzzy reasoning. However, a vast majority of FCM models were constructed manually, i.e., they are based on domain experts' knowledge.…”

Section: Structure Of the Approachmentioning

confidence: 99%

Implementation of Fuzzy Cognitive Maps Using Fuzzy Neural Network and Application in Prediction of Time Series

Song

Miao²,

Roel

et al. 2009

IEEE Trans. Fuzzy Syst.

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The fuzzy cognitive map (FCM) has gradually emerged as a powerful paradigm for knowledge representation and a simulation mechanism that is applicable to numerous research and application fields. However, since efficient methods to determine the states of the investigated system and to quantify causalities that are the very foundations of FCM theory are lacking, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to the model subjectivity and difficulties with assessing its reliability. In this paper, we proposed a fuzzy neural network to enhance the learning ability of FCMs. Our approach incorporates the inference mechanism of conventional FCMs with the determination of membership functions, as well as the quantification of causalities. In this manner, FCM models of the investigated systems can automatically be constructed from data and, therefore, operate with less human intervention. In the employed fuzzy neural network, the concept of mutual subsethood is used to describe the causalities, which provides more transparent interpretation for causalities in FCMs. The effectiveness of the proposed approach in handling the prediction of time series is demonstrated through many numerical simulations.Index Terms-Fuzzy cognitive maps (FCMs), fuzzy neural network, fuzzy-rule identification, mutual subsethood.

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“…According to formula (3), (k) ℜ can be written as, (10) where N indicates the number of fuzzy events in the PFCM, and . In PFCM, weight matrix also represents causal relationships between fuzzy events which is expressed as …”

Section: E Inference Process Of Pfcmsmentioning

confidence: 99%

“…Compared with traditional expert systems and neural networks, FCM has several desirable properties [33] [10], such as abstraction, flexibility, adaptability and fuzzy reasoning. Therefore, FCM has gained considerable research interests [13] [14] [15], and has been applied in various scientific fields, e.g.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Fuzzy Cognitive Map

Song

Shen

Chen

et al. 2006

2006 IEEE International Conference on Fuzzy Systems

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In this paper, we present the Probabilistic Fuzzy Cognitive Map (PFCM) which is a novel extension of FCM theory. Each concept in PFCM is extended to a fuzzy event that models not only the fuzzy degree but also the fuzzy probability of both the cause and the effect concepts. PFCM enhances the capability of conventional FCMs to handle both randomness and fuzziness which are necessary to model the uncertainty involved in inference process of complex causal system. A formalized inference process of PFCMs is presented for adjustments on probability of fuzzy events and for dynamic update of causal weights. This enables PFCM to synthetically analyze the impacts of randomness and fuzziness on causal inference process. The simulation result shows a good match to the above features of PFCM. PFCM, as an initial attempt, provides a heuristic approach to model the uncertainty of complex causal systems and opens a collection of interesting research issues for further research.Index Terms-fuzzy cognitive map, probabilistic fuzzy cognitive map, causal inference process, dynamic property, fuzzy event.

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Fuzzy cognitive state map vs markovian modeling of user's web behavior

Cited by 3 publications

References 14 publications

The Application of Fuzzy Cognitive Map in Soft System Methodology

The Application of Fuzzy Cognitive Map in Soft System Methodology

Implementation of Fuzzy Cognitive Maps Using Fuzzy Neural Network and Application in Prediction of Time Series

Probabilistic Fuzzy Cognitive Map

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