Inference via fuzzy belief Petri nets (original) (raw)
Related papers
Inference via Fuzzy Belief Networks
Computer Applications in Industry and Engineering, 2002
The power of belief networks lies in its connective edges where the influences are bidirectional. While Bayesian methods capture bidirectional influences, we propose a simpler and faster method o f inferencing fr om nodal observations that uses bidirectional fuzzy influences that are propagated via fuzzy set membership functions. We need neither the conditional probability tables nor constraining mathematical structure that
Fuzzy reasoning supported by Petri nets
IEEE Transactions on Fuzzy Systems, 1994
We develop a representational model for the knowledge base (KB) of fuzzy production systems with rule chaining based on the Petri net formalism. The model presents the execution of a KB following a data driven strategy based on the sup-min compositional rule of Inference. In this connection, algorithms characterizing different situations have been described, including the case where the KB is characterized by complete information about all the input variables and the case where it is characterized by ignorance of some of these variables. For this last situation we develop a process of "incremental reasoning"; this process allows the KB to take information about previously unknown values into consideration as soon as such information becomes available. Furthermore, as compared to other solutions, the rule chaining mechanism we introduce is more flexible, and the description of the rules more generic. The com utational complexity of these algorithms is 0 ((+ M + N) R) for the "complete information" case and O ((M + N) R 2) and 0 (2 (M + N) R 2) for the other cases, where R is the number of fuzzy conditional statements of the KB, M and N the maximum number of antecedents and consequents in the rules and C the number of chaining transitions in the KB representation. f I. INTRODUCTION NE OF THE MOST successful applications of fuzzy 0 logic has been in the area of processes control [1],[2].
1999
In this paper we investigate how the observation of symptoms which do not completely match a modeled fault can be used to find the most likely fault ‐ and the degree to which this fault occurs. We start out by setting up fuzzy causal diagrams and then show how with the use of a proper operator the arcs of the causal diagram can be reversed. We introduce a graphical representation for fuzzy belief nets (FBN) and show how both AND and OR connected antecedents and consequents of rules can be accommodated. The paper concludes with an illustrative diagnostic example.
Adaptive fuzzy petri nets for dynamic knowledge representation and inference
Expert Systems with Applications, 2000
Knowledge in some fields like Medicine, Science and Engineering is very dynamic because of the continuous contributions of research and development. Therefore, it would be very useful to design knowledge-based systems capable to be adjusted like human cognition and thinking, according to knowledge dynamics. Aiming at this objective, a more generalized fuzzy Petri net model for expert systems is proposed, which is called AFPN (Adaptive Fuzzy Petri Nets). This model has both the features of a fuzzy Petri net and the learning ability of a neural network. Being trained, an AFPN model can be used for dynamic knowledge representation and inference. After the introduction of the AFPN model, the reasoning algorithm and the weight learning algorithm are developed. An example is included as an illustration. ᭧
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2000
In this paper we investigate how the observation of symptoms which do not completely match a modeled fault can be used to find the most likely fault -and the degree to which this fault occurs. We start out by setting up fuzzy causal diagrams and then show how with the use of a proper operator the arcs of the causal diagram can be reversed. We introduce a graphical representation for fuzzy belief nets (FBN) and show how both AND and OR connected antecedents and consequents of rules can be accommodated. The paper concludes with an illustrative diagnostic example.
Certain Bayesian Network based on Fuzzy knowledge Bases
2012
In this paper, we are trying to examine trade offs between fuzzy logic and certain Bayesian networks and we propose to combine their respective advantages into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of fuzzy random variables. This paper deals with different definitions and classifications of uncertainty, sources of uncertainty, and theories and methodologies presented to deal with uncertainty. Fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic. Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge
Parameterised Fuzzy Petri Nets for Knowledge Representation and Reasoning
2013
The paper presents a new methodology for knowledge representation and reasoning based on parameterised fuzzy Petri nets. Recently, this net model has been proposed as a natural extension of generalised fuzzy Petri nets. The new class extends the generalised fuzzy Petri nets by introducing two parameterised families of sums and products, which are supposed to provide the suitable t-norms and s-norms. The nature of the fuzzy reasoning realised by a given net model changes variously depending on tand/or s-norms to be used. However, it is very difficult to select a suitable tand/or s-norm function for actual applications. Therefore, we proposed to use in the net model parameterised families of sums and products, which nature change variously depending on the values of the parameters. Taking into account this aspect, we can say that the parameterised fuzzy Petri nets are more flexible than the classical fuzzy Petri nets, because they allow to define the parameterised input/output operators. Moreover, the choice of suitable operators for a given reasoning process and the speed of reasoning process are very important, especially in real-time decision support systems. Some advantages of the proposed methodology are shown in its application in train traffic control decision support.
A reasoning algorithm for high-level fuzzy Petri nets
IEEE Transactions on Fuzzy Systems, 1996
In this paper, we introduce an automated procedure for extracting information from knowledge bases that contain fuzzy production rules. The knowledge bases considered here are modeled using the high-level fuzzy Petri nets proposed by the authors in the past. Extensions to the high-level fuzzy Petri net model are given to include the representation of partial sources of infnrmation. The case of rules with more than one variable in the consequent is also discussed. A reasoning algorithm based on the high-level fuzzy Petri net model is presented. The algorithm consists of the extraction of a subnet and an evaluation process. In the evaluation process, several fuzzy inference methods can be applied. The proposed algorithm is similar to another procedure suggested by Yager [20], with advantages concerning the knowledge-base searching when gathering the relevant information to answer a particular kind of query.