Extended Fuzzy Petri Nets for Decision Support (original) (raw)
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Classification of Fuzzy Petri nets, and their applications
Petri Net (PN) has proven to be effective graphical, mathematical, simulation, and control tool for Discrete Event Systems (DES). But, with the growth in the complexity of modern industrial, and communication systems, PN found themselves inadequate to address the problems of uncertainty, and imprecision in data. This gave rise to amalgamation of Fuzzy logic with Petri nets and a new tool emerged with the name of Fuzzy Petri Nets (FPN).
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].
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. ᭧
2004
In this paper an overview of the most important artificial intelligence diagnosis tools is given. For each tool, we focus on diagnosis principles and on their advantages and disadvantages. That allows us to extract four important points that a diagnosis tool should fulfilled. Using these results, we propose a tool based on fuzzy Petri nets and a tool based on neuro-fuzzy systems, which allow to make a diagnosis using a model easy to build and that take into account the uncertainties of maintenance knowledge. These tools provide abductive approaches of fault propagations research with an efficient localization and a characterization of the fault origin. At the end, we apply our tools on a comparative example of a flexible system diagnosis.
A Fuzzy Logic Approach to Decision Support in Medicine
Bangladesh Journal of …, 2011
We present an application of fuzzy logic for the development of a decision support system in medicine. As a working example we model the prognosis of a patient suffering hearth failure treated with beta-blockers. It is shown how the basic rules, based on expert experience, are represented in a system based on fuzzy logic.
Use of Fuzzy Logic Based Decision Support Systems in Medicine
Studies on Ethno-Medicine, 2016
The complexity of the problems that are faced within people's decision-making process can reveal a variety of challenges in the solution process. The increasing complexity of the events faced, makes the decisionmaking more difficult. Therefore, recently, a trend has occurred in advanced technologies such as decision support systems (DSS). DSS offer alternative solutions with a flexible and objective perspective to researchers in various fields, particularly in the fields of medicine and life science. DSS can be designed using artificial intelligence based methods such as fuzzy logic (FL), and artificial neural networks (ANN). Nowadays, the fuzzy logic-based DSS in the medical field such as the disease diagnosis, the determination of appropriate treatment, the costs a nd so on, including issues in making clinical decisions are widely used, and successfully applied. In this study, FL-based DSS have been introduced, and different applications used in the medicine field have been given. The mea n of the success level of the FL-based DSS was determined to be ninety percent. FL-based DSS has been providi ng a significant contribution to disease diagnosis in the examined studies.
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.