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Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs), where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN) reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.

A supervisory hybrid system may be modeled from two levels: logic level (upper) and continuous level (lower). In this paper, adaptive fuzzy Petri nets and neural networks are combined together for supervisory hybrid system modeling. Adaptive Fuzzy Petri Net is adopted to model the supervisory logic parts, and dynamic neural networks are applied to continuous parts. Two hybrid system examples are illustrated to show the effective of the method.

This paper studies diagnosis-aid systems that use artificial intelligence tools. This kind of system is very interesting in an uncertain industrial environment, especially flexible production systems. An overview of the most important artificial intelligence diagnosis tools is given. For each tool, we focus on diagnosis principles and its advantages and disadvantages. That allows us to extract four important points that a diagnosis tool should fulfil. Using these results, we propose a tool based on fuzzy Petri nets which allows to make a diagnosis using a model that is easy to build and that takes into account the uncertainties of maintenance knowledge. This tool provides abductive approaches of a fault propagation system with efficient localization and characterization of the fault origin. We apply our tool to an illustrative example of flexible system diagnosis.

A model using Fuzzy Petri Nets (FPN) is developed for assessing the risk associated with the post-implementation phase or the usage phase of Enterprise Resource Planning (ERP) in Small and Medium Enterprises. This paper has identified a well defined set of risk factors from the existing research literature. The usage of FPN is because of its simplicity of usage and the efficiency in quantifying the risks inherent in the post-implementation or the usage stage of ERP adoption by SMEs. The model represented in the report will be an authentic character from a project risk management perspective to validate and quantify the risk inherent in the usage phase of ERP adoption.