As the computing world moves from the information age into the knowledge-base age, it is beneficial to induce knowledge from the information superhighway formed from the Internet and intranet. Knowledge discovery in databases is defined as the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in databases. The knowledge ac...

Two simple but practical production systems are modeled using Petri Nets. Petri-net models are very useful in finding semantic errors like generalization error and missing conditions. In addition, reasoning with insufficient input information is formulated to yield meaningful results. In both rule-base verification and reasoning with insufficient data, heuristic methods like backward-chained re...

The concept of a Petri net, a tool for the study of certain discrete dynamical systems, was invented in 1939 by Carl Adam Petri. In the attempt to characterize Boolean Petri nets, we discovered a subclass of Boolean Petri net called the crisp Boolean Petri net, viz. , the one that generates every binary vector as its marking vectors exactly once. In this paper, the construction of a crisp Boole...

Models of fuzzy Petri net (FPN) are defined based on generating rules of knowledge base and the Petri nets semantics description of logical rules are presented. Two kinds of reasoning patterns are considered. First, the beliefs of initial propositions are known and their beliefs of noninitial propositions need to be computed. Second, the beliefs of initial propositions are known and the beliefs...

In highly competitive industrial market, the concept of failure analysis is an unavoidable fact in complex industrial systems. Reliability of such systems not only depends on the reliability of each element of these systems, but also depends on occurrence of sequence of failures. In this paper, a novel approach to sequential failure analysis is proposed which is based upon fuzzy logic and the c...

State of the art approaches for modelling biological systems can be classified as qualitative or quantitative. Qualitative models are relatively simple and require only basic knowledge of the system, but may only be used to get a rough image about the system’s dynamics. On the other hand, quantitative models are able to mimic accurate dynamical properties of observed system, but require accurat...