Rule based fuzzy cognitive maps - expressing time in qualitative system dynamics

Carvalho, João Paulo; Tome, J.A.B.

doi:10.1109/fuzz.2001.1007303

Cited by 50 publications

(26 citation statements)

References 3 publications

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“…And all of their interactions among concepts, relations, directional connections and weights compose a dynamic network. In Fig.1 Fig.1 represents that there exists first sequential relation and the time-delay causal relation from C 6 and C 7 to C 5 , and there also exists conditional probabilistic causal relation from C 5 to C 2 , from C 5 to C 3 …”

Section: The Mathematical Modelmentioning

confidence: 96%

Rule-Based FCM: A Relational Mapping Model

Chen

2005

Discovery Science

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Abstract. Rule-Based Fuzzy Cognitive Map (RBFCM) is proposed as an evolution of Fuzzy Causal Maps (FCM) to allow more complete and complex representation of cognition so that relations other than monotonic causality are made possible. This paper shows how RBFCM can be viewed in the context of relation algebra, and proposes a novel model for representing and reasoning causal knowledge relation. The mapping model and rules are introduced to infer three kinds of causal relations that FCM can't support. Capability analysis shows that our model is much better than FCM in emulating real world. MotivationFuzzy Conceptual Maps have become an important means for drawing a graphical representation of a system , and connecting the state concepts (variables) in the system by links that symbolize cause and effect relations, and have been used in simulating process, forecasting or decision support, etc. Though FCMs have many desirable properties, they have some major limitations [4]. For example, .FCMs can't provide the inference of sequential relations or time-delay causal relations because all the interaction of FCMs' concepts is synchronous, and can't provide the inference of conditional probabilistic causal relations. Their inference results in some intelligent systems are usually distorted.Some authors have tried to extend FCMs to include time, and they developed systems such as "Extended FCMs" (Hagiwara [2]) and rule-based FCMs (Carvalho [3]).But they can't support conditional probabilistic causal relations. Neural Cognitive Map (NCM [5]) are presented to solve complex causal relations, but NCM needs much training data that are difficult to be obtained in some intelligent systems, and time-delay causal relations as well as sequential relations are difficult to be found by neural networks.Our model proposes a novel model for representing and reasoning causal knowledge relation. The mapping model and rules are introduced to infer three kinds of causal relations including sequential relation, time-delay causal relations, and conditional probabilistic causal relations that FCM can't support. The Mathematical ModelIn our model, causal knowledge is in the form of concepts, relations, directional connections and weights. Fig.1 describes the cause-effect relation mapping about terror

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Section: The Mathematical Modelmentioning

confidence: 96%

Rule-Based FCM: A Relational Mapping Model

Chen

2005

Discovery Science

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“…To allow the representation of time flow, delays, and the inhibition of certain relations when they have no influence on a given instant, changes were made to the engine of RB-FCM. More details regarding RB-FCM time mechanisms can be found in works by the present author [8,11].…”

Section: Expressing Time In Dynamic Cognitive Mapsmentioning

confidence: 98%

“…One can also mention the fact that oil price variation has a delayed effect in inflation. RB-FCM provide mechanisms to model these kinds of timing issues [8,11]. This kind of qualitative approach was used throughout the model when causal relations were involved.…”

Section: Qualitative Modeling Of a Qualitative Dynamic Systemmentioning

confidence: 99%

“…Concepts are fuzzy variables described by linguistic terms, and Relations are defined with fuzzy rule bases. RB-FCM are essentially iterative fuzzy rule based systems where we added fuzzy mechanisms to deal with feedback, introduced timing mechanisms [8] and new ways to deal with uncertainty propagation, and were we defined several kinds of Concept relations (Causal, Inference, Alternatives, Probabilistic, Opposition, Conjunction, etc. [2,5]) to cope with the complexity and diversity of the dynamic qualitative systems we are trying to model.…”

Section: Rule Based Fuzzy Cognitive Mapsmentioning

confidence: 99%

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Rule Based Fuzzy Cognitive Maps in Humanities, Social Sciences and Economics

Carvalho¹

2011

Soft Computing in Humanities and Social Sciences

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“…A more sophisticated approach has been proposed by Carvalho and Tomé: Rule-Based Fuzzy Cognitive Maps (RB-FCM) [4], [6], [5], [7]. Such an FCM consists of fuzzy nodes and a rule base which relates nodes to each other.…”

Section: Limitationsmentioning

confidence: 99%

K2F – A Novel Framework for Converting Fuzzy Cognitive Maps into Rule-Based Fuzzy Inference Systems

Krüger

2010

Artificial Intelligence and Soft Computing

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Abstract. This paper focuses on a novel methodological framework for converting a Fuzzy Cognitive Map into a network of rule-based Fuzzy Inference Systems. Furthermore, it allows to obtain a crisp value representing an arbitrary parameter of the complex system's model. This way the system provides a quantitative answer without employing an exact mathematical model. This paper also outlines a first possible application area: the valuation of investments in high-technology ventures. A field in which usually conventional quantitative and retrospective measures usually do not deliver satisfying results due to the complexity of future-oriented risk prognosis and the lack of quantitative data.

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Rule based fuzzy cognitive maps - expressing time in qualitative system dynamics

Cited by 50 publications

References 3 publications

Rule-Based FCM: A Relational Mapping Model

Rule-Based FCM: A Relational Mapping Model

Rule Based Fuzzy Cognitive Maps in Humanities, Social Sciences and Economics

K2F – A Novel Framework for Converting Fuzzy Cognitive Maps into Rule-Based Fuzzy Inference Systems

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