Victor R. L. Shen

Knowledge Representation Using High-Level Fuzzy Petri Nets

This correspondence presents a high-level fuzzy Petri net (HLFPN) model to represent the fuzzy production rules of a knowledge-based system, where a fuzzy production rule is the one that describes the fuzzy relation between the antecedent and the consequent. The HLFPN can be used to model fuzzy IF-THEN rules and IF-THEN-ELSE rules, where the fuzzy truth values of the propositions are restricted to [0, 1]. Based on the HLFPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. In this correspondence, a novel model to represent fuzzy knowledge is developed. When compared with other related models, the HLFPN model preserves several significant advantages. Finally, main results are presented in the form of eight properties and are supported by a comparison with other existing algorithms

Reinforcement learning for high-level fuzzy Petri nets

The author has developed a reinforcement learning algorithm for the high-level fuzzy Petri net (HLFPN) models in order to perform structure and parameter learning simultaneously. In addition to the HLFPN itself, the difference and similarity among a variety of subclasses concerning Petri nets are also discussed. As compared with the fuzzy adaptive learning control network (FALCON), the HLFPN model preserves the advantages that: 1) it offers more flexible learning capability because it is able to model both IF-THEN and IF-THEN-ELSE rules; 2) it allows multiple heterogeneous outputs to be drawn if they exist; 3) it offers a more compact data structure for fuzzy production rules so as to save information storage; and 4) it is able to learn faster due to its structural reduction. Finally, main results are presented in the form of seven propositions and supported by some experiments.

Supervised and Unsupervised Learning by Using Petri Nets

Artificial neural networks (ANN) are developed for highly parallel and distributed systems. These systems are able to learn from experience and to perform inferences. Although Petri nets (PNs) were modified to be ANN-like multilayered architectures for fuzzy reasoning, some researchers have paid more attention to the PN-based learning so far. In this paper, we have developed supervised and unsupervised learning algorithms for the machine learning PN (MLPN) models in order to make them fully trainable and to remedy the difficulties encountered by ANN. When compared with ANN, the MLPN model shows some significant advantages. Main results are presented in the form of five observations and supported by some experiments.

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.

Verification of Knowledge-Based Systems Using Predicate/Transition Nets

As expert-system technology gains broader acceptance, the need to build and maintain large-scale knowledge-based systems (KBSs) will assume greater importance. Traditional approaches to KBS verification generally contain no predicate/transition (PrT) net models, thus making them slow for the large-scale KBS with chained errors. This paper proposes an attractive alternative to KBS verification, in which the KBS is modeled as a PrT-net model. Then, the least fixpoint semantics of the PrT-net model can be introduced into the KBS for the purpose of speeding up the computations of the KBSs. The significance of this paper is that seven propositions are formulated to detect errors of redundancy, subsumption, unnecessary condition, circularity, inconsistency, dead end, and unreachable goal. Thus, the performance of a computer-aided-design tool for KBSs can be improved to some extent. Meanwhile, specification languages, including Programming in Logic, Frame-and-Rule-Oriented Requirements Specification Language, and the like, are suitable to this approach.

Weighted fuzzy interpolative reasoning systems based on interval type-2 fuzzy sets

Abstract In this paper, we present a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. We also apply the proposed weighted fuzzy interpolative reasoning method to deal with the truck backer-upper control problem. The proposed method satisfies the seven evaluation indices for fuzzy interpolative reasoning. The experimental results show that the proposed method outperforms the existing methods. It provides us with a useful way for dealing with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

A novel application of grey system theory to information security (Part I)

This study applies the grey data generating techniques in grey system theory on a novel cryptosystem, which is guiding a new research in the field of information security. In this paper, we present the concepts of sum-lock, difference-lock, sum-ladder, and difference-ladder. Using these concepts, we can obtain a cryptosystem with lock generation and sum-difference mixed ladder. The cryptographic algorithms for our cryptosystem are also presented and an illustrative example is used to verify it.

Requirements specification and analysis of digital systems using fuzzy and marked Petri nets

Fuzzy information often appears in the system requirements. Fuzzy Petri nets (FPN) are Petri nets in which certain fuzzy truth values are assigned to its transitions. We show how the FPN model can be used for formal specification and verification of digital systems. The consistent FPN model is actually a state machine, from which we can obtain a consistent marked Petri net (MPN) model. Based on the consistent MPN model, the hardware prototype at register transfer level can be easily induced by using the optimization rules. Finally, main results are presented in the form of three theorems and are supported by some experiments.

A PN-based approach to the high-level synthesis of digital systems

A Petri net (PN)-based approach associated with object-oriented technique is proposed to support the specification, analysis, and design of digital systems. Starting from system level to register-transfer level (RTL), the marked Petri net (MPN) with colored tokens is well applied to capture the designers ideas and to present the systems behavior graphically. Through the net model, reachability analysis technique is employed to formally verify the digital system designed. Hence, using the behavioral properties--liveness (i.e. absence of deadlock) and safety (i.e. absence of overflow) of the net model can avoid the hardware system from deadlocks and hazards, respectively. From the live and safe MPN model we can obtain the desired hardware prototype at RTL by using the system optimization rules and object-oriented model checking. Furthermore, a time Petri net (TPN) model can be used to check the time consistency among events. This PN-based modeling approach is superior to the current techniques for requirements analysis. Finally, main results are presented in the form of four properties and supported by some experiments.

Correctness in hierarchical knowledge-based requirements

As expert system technology gains broader acceptance, the need to build and maintain large-scale knowledge bases will assume greater importance. Traditional approaches to knowledge-based systems (KBSs) verification have generally adopted a pairwise comparison of rules, making them slow for large-scale KBSs. This paper introduces the least fixpoint semantics of a predicate/transition (pr/t) net model into the KBSs for the purposes of speeding up the computation and saving the design time of KBSs. An efficient fault diagnosis algorithm is presented to locate some fault(s) made in the KBS design. The significance of this work is that frame- and rule-based hardware description language (FARHDL) can easily form a KBS, and the pr/t net model provides a T-invariant technique to verify the correctness of KBS requirements. Thus, the performance of a computer-aided design (CAD) tool for digital systems can be improved to some extent.