A vehicular traffic congestion predictor system using Mamdani fuzzy inference

The process of traffic control systems significantly relies on the immediate detection of breakdown states. As a result of their crisp (non-fuzzy) based calculation procedures, conventional traffic estimators and predictors cannot effectively model traffic states. In fact, these methods are characterized by exact features, while traffic is defined by uncertain variables with vague properties. Furthermore, typical numerical methodologies have constraints on evaluating the overall system status in heterogeneous and convoluted networks mainly due to the absence of reliable and real-time data. This study develops a fuzzy inference system that uses data from the Hungarian freeway networks for predicting the severity of congestion in this complex network. Congestion severity is considered the output variable, and traffic flow along with the length and the number of lanes of each section are assigned as input variables. Seventy-five fuzzy production rules were generated using accessible datasets, percentile distribution, and experts' consensus. The MATLAB fuzzy logic toolbox simulates the designed model and analysis steps. According to available resources, the results demonstrate linkages among input variables. Analyses are also used to construct intelligent traffic modeling systems and further service-related planning.

A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

Fuzzy multivariate forecasting of the implementation of regional development programs for the agro-industrial complex

The article is devoted to the problems of improving digital intellectual tools for managing the implementation of socio-economic and technological programs aimed at developing the agro-industrial cluster of the regional economy. The aim of the work is to develop a procedure for forecasting the implementation of programs based on the data of the previous stages and knowledge, reflecting the specifics of agricultural production. To describe the indicators of the current and projected state of the regional agro-industrial complex, it is proposed to use the apparatus of the theory of linguistic variables, which makes it possible to use expert technologies for filling the knowledge base and allows us to take into account the high level of uncertainty characteristic of the agricultural market. The links between current and projected performance are represented by fuzzy production rules. The fuzzy inference procedure used in forecasting (based on the Mamdani algorithm) is built in the form of an interpreted fuzzy multilayer neural network. The preliminary results of using the developed procedure as part of a research prototype of an information-analytical system may indicate its effectiveness. The practical significance of the developed toolkit is due to the possibility of its use as a means of intellectual support for making scientifically grounded management decisions on the implementation (taking into account possible adjustments) of development programs for the regional agro-industrial complex.

The Usage of GIS in Realizing Engineering Education Quality

The chapter has considered research and instructional methodology aspects for development of methodological, informational, and instrumental, ensuring of the education quality management system which are necessary to be taken into account in modern conditions. Mathematical bases of the geoinformation system application for monitoring of the education process realization quality have been developed. Model, method, and algorithm for quality assessment of the educational process realization in institutions have been unfolded. A way of representing some fuzzy production rules in solving application tasks of fuzzy modeling and executing the process of approximate reasoning on educational risks has been introduced. A fuzzy production system of educational risk analysis on the basis of using modified fuzzy Petri nets has been realized. Analysis of possibilities to apply suggested approaches for monitoring of institutions at various levels has been conducted.

The Usage of GIS in Realizing Engineering Education Quality

The chapter has considered research and instructional methodology aspects for development of methodological, informational, and instrumental, ensuring of the education quality management system which are necessary to be taken into account in modern conditions. Mathematical bases of the geoinformation system application for monitoring of the education process realization quality have been developed. Model, method, and algorithm for quality assessment of the educational process realization in institutions have been unfolded. A way of representing some fuzzy production rules in solving application tasks of fuzzy modeling and executing the process of approximate reasoning on educational risks has been introduced. A fuzzy production system of educational risk analysis on the basis of using modified fuzzy Petri nets has been realized. Analysis of possibilities to apply suggested approaches for monitoring of institutions at various levels has been conducted.

An Automatic Algorithm to Generate a Reachability Tree for Large-Scale Fuzzy Petri Net by And/Or Graph

Fuzzy Petri net (FPN) is widely used to repre sent, model and analyse knowledge-based systems (KBSs). Meanwhile, a reachability tree is an important tool to fully represent the flow relationship of FPN and is widely applied to implement inference in industrial areas. However, the traditional reachability ignores recording the dependence relationships (‘and/or’ relationship) among the places in the neighbouring layers. This paper develops a modified reachability tree based on an and/or graph and presents a three-phase generation algorithm to model the reachability tree for the corresponding FPN automatically via fuzzy production rules (FPRs). Four cases are used to verify the correctness and feasibility of the proposed algorithm from different viewpoints, such as general FPRs, FPRs with a condition-sharing situation, FPRs with a conclusion-sharing situation, and FPRs with multi-conclusions. Simulation results reveal that the proposed approach has the ability to automatically generate the reachability tree for the corresponding FPN correctly.

EVALUATION OF OPERATION QUALITY OF CELLULAR COMMUNICATION SYSTEMS USING FUZZY CONCLUSION ALGORITHM

Quality of mobile system communication is significantly defined by completeness and reliability of information on a status of objects of radio-electronic means. In most cases, the probability of decline in quality increases with untimely response of field services to the existing back couplings of the control system and the analysis of parameters of quality. Modern objects of radio-electronic means are known to be difficult systems having technical intelligence. Therefore, when processing the arriving information on statuses from such objects there happen such situations when use of traditional methods brings to a row of difficulties in their analysis. It occurs for the reasons of insufficiency of prior information, multiparametricity, volume of computing character, a non-standard of situations, a subjective factor of operators. In this regard for processing of such information it is offered to use the theory of fuzzy sets. Parameters of monitoring in the system of mobile communication have been determined. Using the field power level as a key parameter is justified because the whole range of the related secondary parameters of communication quality on input of a receiving device depends on signal power of a communication system in a point of reception. The fuzzy production model has been developed. Each parameter is provided to a linguistic variable for which there is defined a term - set of values. The fuzzy production rules of the model are realized using Fuzzy Logic Toolbox of the software MATLAB on the basis of Mamdani's algorithm.

Fuzzy interpretation for temporal-difference learning in anomaly detection problems

Nowadays, information control systems based on databases develop dynamically worldwide. These systems are extensively implemented into dispatching control systems for railways, intrusion detection systems for computer security and other domains centered on big data analysis. Here, one of the main tasks is the detection and prediction of temporal anomalies, which could be a signal leading to significant (and often critical) actionable information. This paper proposes the new anomaly prevent detection technique, which allows for determining the predictive temporal structures. Presented approach is based on a hybridization of stochastic Markov reward model by using fuzzy production rules, which allow to correct Markov information based on expert knowledge about the process dynamics as well as Markov’s intuition about the probable anomaly occurring. The paper provides experiments showing the efficacy of detection and prediction. In addition, the analogy between new framework and temporal-difference learning for sequence anomaly detection is graphically illustrated.

Application of Fuzzy Petri Nets in Hydraulic Pump Fault Diagnosis

In this paper, an approach of fuzzy Petri nets (FPN) is proposed to simulate the fault spreading and diagnosis of hydraulic pump. First, the fuzzy production rules and the definition of FPN were briefly introduced. Then, its knowledge reasoning process and the matrix operations based on an algorithm were conducted, which makes full use of its parallel reasoning ability and makes it simpler and easier to implement. Finally, a case of hydraulic pump fault diagnosis with FPN was presented in detail, for illustrating the interest of the proposed modeling and analysis algorithm.

An Fuzzy Petri Net Model of Accident Evolution Process of Coal and Gas Outburst

This article has proposed an accident evolution model of coal and gas outburst on the basis of fuzzy production rules, because the coal and gas outburst accident has the characteristic of fuzziness and uncertainty. Firstly, adverse searching method is used to reduce the initial model so as to decrease the size of the inference network and accelerate reasoning rate. And then fuzzy inference is carried out by use of parallel inference algorithms of great algebra in order to ensure more accurate infer conclusions. At the end of this article, the validity of the model is verified by an engineering example. From the conclusion, it can be found that this model can well simulate the status and role of a number of risk factors during the whole coal and gas outburst accident evolution process.