Fuzzy Reasoning Process Research Articles

Interval Type-2 Fuzzy Relation Matrix Model via Semitensor Product

A new modeling idea of interval type-2 fuzzy logic system (IT2 FLS) is proposed on the basis of matrix semi-tensor product (STP) fuzzy representation technique, to construct an interval type-2 fuzzy relationship matrix (IT2 FRM) model. The model naturally inherits the advantages of STP theory, which enables the fuzzy logic reasoning process to be translated into algebraic form for computation. In most cases, the fuzzy rules in the IT2 FRM are generally unknown. To address the above situation, the parameters of this model can be trained with observed data pairs. Firstly, the fuzzy rules are generated by the space division method, and the initial parameters of the model are determined. Secondly, the parameters in the model are trained using the steepest descent algorithm. Finally, the IT2 FRM model is validated by simulation, and the results demonstrate that the IT2 FLS can be designed using the STP method.

Application of Fuzzy Case-Based Reasoning and Fuzzy Analytic Hierarchy Process for Machining Cutter Planning and Control

Cutter planning and control are the crucial problems in machining processes. The current literature indicates that the issue of cutter planning and control problem was not adequately researched in the past in a metal-cutting process. Usually, cutter planning and control problems were addressed using different optimization, simulation, and computer-aided planning (CAP) methods. To bridge this knowledge gap, this study proposed a decision support system (DSS) that can integrate fuzzy case-based reasoning (F-CBR) and fuzzy analytic hierarchy process (F-AHP) methods. This integration was applied to determine hybrid similarity measures between new and prior cases. The study provides new insights into the integration of fuzzy set theory (FST), CBR, and AHP for solving machining cutter planning and control problems. Our proposed system retrieves the best similar prior cases to reuse and adapt them to new order arrivals. A numerical example was illustrated to validate the soundness of the researched DSS.

Research on run-time risk evaluation method based on operating scenario data for autonomous train

Recent years witness the focus of the research of next-generation railways on risk situation awareness and safety decision-making to enhance the autonomy of unmanned trains. However, complex environmental factors make it difficult to assess the risks of train operation. Thus, it is of great necessity to clearly monitor the scenario parameters under which the train control system is designed to work, and to infer real-time risk through the collected scenario data. This paper first clarifies the key scenario parameters that need to be collected during the operation according to the concept of Operational Design Domain (ODD) and operating scenario. The key parameters and their dependencies are used to derive the Dynamic Bayesian Network (DBN) structure. Second, for data probability uncertainty, Fuzzy Set Theory is introduced, within the framework of which a fuzzy dynamic reasoning process is presented by monitoring the scenario data deviation. Finally, a case of real-time risk evaluation and analysis of the accident of Singapore MTR is explicated to demonstrate its contribution to operating data-based runtime risk analysis.

Three-way decision theory based on interval type-2 fuzzy linguistic term sets

This study examines decision theory based on interval type-2 fuzzy sets with linguistic information for the three-way decision approach by addressing the challenge of uncertainty for information analysis and fusion in subjective decision-making processes. First, the interval type-2 fuzzy linguistic term sets (IT2 FLTSs) are defined to represent and normalize the uncertain preference information in linguistic decision-making. Subsequently, perception computing based on computing with words paradigm is introduced to implement information fusion among different decision-makers in the linguistic information-based fuzzy logic reasoning process. Then, a three-way decision (3WD) theory based on IT2 FLTSs with fuzzy neighborhood covering is proposed, and the corresponded tri-partitioning strategies that satisfy Jaccard similarity of membership distributions are given. Finally, 3WD theory is applied to multi-criteria group decision-making with linguistic terms, and the algorithm steps are illustrated by a promising application under the background of coronavirus disease 2019 to reveal the feasibility and practicability of the proposed approach.

Travel route safety estimation based on conflict simulation

With the aim of providing travelers information about the safety levels of selectable routes, it is necessary to develop a method that can properly estimate the safety of alternative travel routes. This paper proposes a conflict-based approach for travel route safety estimation (TRSE). It is developed on the basis of the classical safety evaluation model where both the amount of exposure to safety risk and the risk under unit exposure are measured to estimate the route safety. A combination of a set of dynamic and static factors related to traffic flow characteristics and roadway features are selected to estimate conflict exposure and potential conflict risk. A route-based method is employed where two parallel estimations of conflict are conducted for both the component segments links and intersection turning links. Three machine learning models (i.e., random forest, k-nearest neighbor, and support vector machine) are tested in conflict risk estimation. A fuzzy reasoning process based on the fuzzy logic algorithm is employed to conduct the route safety estimation. The proposed TRSE is tested on a four-horizontal and six-vertical network extracted from a real road network in China. Conflict simulation results were obtained by Vissim and SSAM tools. The results illustrate the practicability and effectiveness of the proposed TRSE approach.

RETRACTED ARTICLE: Research on early warning and monitoring algorithm of financial crisis based on fuzzy cognitive map

Because the financial crisis early warning and monitoring is too complex, most of the current implementation can only alternately choose early warning in accordance with the criteria of monitoring the financial crisis. As a result, the security measures are not in accordance with the actual situation of the system, and cannot be adjusted quickly according to the changes of the system. This paper presents a financial crisis early warning detection algorithm based on fuzzy cognitive map. The relationship between financial data is obtained by fuzzy cognitive map, and the system crisis value is calculated by fuzzy cognitive map reasoning process. Finally, through the financial data of listed companies, the early warning and monitoring effect of the algorithm is verified. The experimental results show that the method is efficient, cost-effective, and can timely and reasonably reflect the crisis state of financial data.

Reasoning over decomposing fuzzy description logic

Abstract A DF-ALC (Decomposing fuzzy ALC) is proposed in this paper to satisfy the need for representing and reasoning with fuzzy ontologies in the context of semantic Web. A DF-ALC is also proposed to satisfy the need for seeing the necessity of decomposing ontology into several sub-ontologies in order to optimize the fuzzy reasoning process. The main contribution of this work is to decompose the axioms of the ontology into sub-axioms according to a degree of certainty which is assigned to the fuzzy concepts and roles. It is also to define the syntax and semantics and to propose a local reasoning algorithm and a way of using gateways to infer between local TBox.

Air quality assessment using a weighted Fuzzy Inference System

Air pollution is a current monitored problem in areas with high population density such as big cities. In this sense, environmental modelling should be accurate in order to generate better air quality evaluations; but in consequence they are complex. Nowadays, the artificial intelligence based on heuristic methods allows assessing air quality parametres, providing a partial solution to this problem. Accordingly, this paper proposes a new evaluation model using fuzzy inferences combined with an Analytic Hierarchy Process, providing a new air quality index. Environmental parametres (PM2.5, PM10, O3, CO, NO2 and SO2) are evaluated according to toxicological levels and then, a fuzzy reasoning process assesses different air quality situations. Additionally, individual weights are computed and assigned according to the pollutant importance on the air evaluation. Finally, the model proposed considers five score stages: excellent, good, regular, bad and dangerous, based on data from the Mexico City Atmospheric Monitoring System (SIMAT). Experimental results show a good performance of the proposed air quality index against those in literature, providing better assessments when weights are assigned according to an importance level in atmosphere pollution.

Adaptive fuzzy interpolation based on ranking values of polygonal fuzzy sets and similarity measures between polygonal fuzzy sets

After the fuzzy interpolative reasoning processes, if two unequal fuzzy interpolative reasoning results are derived (or one derived and another observed) for a consequence variable of a fuzzy rule, then it is called a contradiction in the fuzzy interpolative reasoning process. In recent years, some adaptive fuzzy interpolation methods for sparse fuzzy rule-based systems have been presented to solve contradictions after fuzzy interpolative reasoning processes. In this paper, a new adaptive fuzzy interpolation method is proposed for sparse fuzzy rule-based systems based on ranking values and similarity measures of polygonal fuzzy sets, which can solve contradictions occurred in fuzzy interpolative reasoning processes. We also apply the proposed adaptive fuzzy interpolation method to predict the diarrheal disease rates in roadless villages. The diarrheal disease prediction problem focuses on the issue to measure how the construction of a new road or railway in a previously roadless area may affect the epidemiology of infectious diseases in northern coastal Ecuador. The experimental results show that the proposed adaptive fuzzy interpolation method can overcome the drawbacks of the existing methods.

Development of Decision Support System for the Diagnosis of Arthritis Pain for Rheumatic Fever Patients: Based on the Fuzzy Approach

Developing a Decision Support System (DSS) for Rheumatic Fever (RF) is complex due to the levels of vagueness, complexity and uncertainty management involved, especially when the same arthritis symptoms can indicate multiple diseases. It is this inability to describe observed symptoms precisely that necessitates our approach to developing a Decision Support System (DSS) for diagnosing arthritis pain for RF patients using fuzzy logic. In this paper we describe how fuzzy logic could be applied to the development of a DSS application that could be used for diagnosing arthritis pain (arthritis pain for rheumatic fever patients only) in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our approach employs a knowledge-base that was built using WHO guidelines for diagnosing RF, specialist guidelines from Nepal and a Matlab fuzzy tool box as components to the system development. Mixed membership functions (Triangular and Trapezoidal) are applied for fuzzification and Mamdani-type is used for the fuzzy reasoning process. Input and output parameters are defined based on the fuzzy set rules.

A fuzzy multi-criteria decision support system for evaluating subsea oil pipeline routing criteria in East Malaysia

Selecting suitable pipeline routes is essential to the success of any oil exploration and transportation project, and proper prioritization of the diverse routing criteria is an integral component of the route selection endeavor. However, recent researches reveal that accurate prioritization of routing criteria, particularly in an offshore/subsea environment, has not been sufficiently addressed. With global attention rapidly expanding from terrestrial exploration to offshore and subsea oil pipeline operations, this study addresses the offshore pipeline routing ranking concerns. A hybrid multi-criteria decision support system was developed by integrating fuzzy logic-based approximate reasoning (AR) and Analytic Hierarchy Process (AHP) mathematical models. Twelve diverse criteria and sub-criteria in an oil-rich basin offshore east Malaysia were examined to determine their impact in the selection of an optimal pipeline route in the area. These were subsequently classified into three broad categories: economic, engineering, and environmental routing criteria. The integrated system (AR-AHP) was used to determine the relative importance of the criteria using different fuzzy membership functions and linguistic variables. Final results showed that the environmental criteria in the study area have the highest aggregated priority and influence (61.4 %) followed by the engineering criteria (27.6 %) and the economic criteria (11 %) datasets, respectively. This study thus establishes the fact that preservation of environmental factors is superior to the other strategic criteria in the marine study area.

Development of Intelligent Frequency Drum Washing Machine Based on Fuzzy Control Technology

Aiming at the current energy efficiency of washing machine is not high. The smart inverter washing machine based on fuzzy control technology is designed. Drum washing machine including a content body structure design, programmable controller and the overall program and frequency control module is highlighted. And the generation and fuzzy reasoning process SPWM signals and other issues are discussed in detail. The results show that the use of fuzzy control technology frequency control can not only save water and energy, but also meet the modern requirements of a variety of special laundry, in line with the development trend of the washing machine technology.

Research on Construction Project Cost Control Based on Fuzzy Control

Traditional cost control methods include cost estimation, planning, accounting, and repetitive control. However, they are not able to desirably solve large-quantity, time-dependent, and other non-mathematic issues related to cost control. This paper studied cost control by means of fuzzy control technology: establishing the cost fuzzy control system, fuzzy reasoning, and fuzzy control process (rule). By case analysis, it is found that the fuzzy control system of cost control can output control measures based on different input information, that it can accordingly choose and process adequate schemes of management, and that applications of exact mathematical models concerned are not necessary. This method is simple to be applied. Its application is efficient for scientific and computerized management of cost control. It can also be used to improve the level of project cost control.

Analysis on the Stability of Reservoir Soil Slope Based on Fuzzy Artificial Neural Network

Owing to the fact that the relation between the reservoir soil slope stability and its influencing factors is complicated and fuzzy, a method-fuzzy neural network to analyze the reservoir soil slope stability is proposed. The method infuses fuzzy reasoning process into the structure of neural network, makes the physical meaning of neuron and weight of neural network clear, reduces the process of regulation match, raises the speed of reasoning and improves greatly the self-adaption capacity of the system. In the end, the fuzzy neural network model is trained and tested by the collected 21 cases of soil slope data samples. The result proves that the fuzzy neural network is a valid method, which has significant advantages over general BP neural network model in analyzing effectiveness and quality.

Risk Analysis in Construction Sites Using Fuzzy Reasoning and Fuzzy Analytic Hierarchy Process

Abstract In this paper a risk assessment process is discussed by considering all kinds of body part wise injuries which are commonly occurred in construction sites. The risk corresponding to each body part wise injuries are measured using a combination of fuzzy reasoning and fuzzy-analytic hierarchy process. In the proposed methodology, risk scores corresponding to each body part wise injuries are measured first using fuzzy reasoning. Then associated weights of each body part wise injuries are calculated on the basis of current safety situation using fuzzy-analytic hierarchy process. The overall risk score of the concerned construction sites are evaluated using sum of product method. This risk assessment system is advantageous in dealing with both qualitative and quantitative risk data due to its capability of capturing vague and possibilistic data in an effective and efficient manner and which may help safety professionals and engineers to improve their safety management system of the construction sites. A case study on risk analysis of an Indian construction company is used to illustrate the application potentiality of the proposed approach.

Prioritising security vulnerabilities in ports

Ports are exposed to various risks in their internal operations and external interactions with inland transport carriers and sea-going vessels within maritime logistics systems. While conventional safety management techniques may be capable of dealing with accidental, hazard-based risks in port, new vulnerability analysis methods are urgently required for tackling those caused by threats such as terrorist attacks. The motivation for identifying the vulnerabilities is the need for prioritising activities and resources on port security investments and risk reduction processes. This paper develops an advanced threat-based criticality analysis methodology designed for the identification and prioritisation of vulnerable port facilities under uncertainties. The model relies on the combination of fuzzy Bayesian reasoning and analytical hierarchy process (AHP) analysis in a complementary way so as to facilitate the treatment of uncertainty in data, thus realising effective quantitative analysis of the vulnerabilities under different threat modes in ports. The outcomes can be used either as a stand-alone technique for prioritising critical systems such as port facilitates with high values and significant functions or as part of an integrated decision making method for evaluating the effectiveness of security control options.

A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.

A Compensatory Fuzzy Neural Network Modeling Method Based on Particle Swarm Clustering

According to modeling problem for complex systems, a compensatory fuzzy neural network (CFNN) modeling method based on particle swarm clustering is proposed: the particle swarm clustering is used to automatically separate the space of input-output data, obtain the numbers of inference rules of fuzzy model and find fuzzy rules. Based on the rules, we modified fuzzy reasoning process and established initial structure of compensatory fuzzy neural network. Then using adaptive rate algorithm optimized initial network parameters, which can obtain a faster training speed and more precision. Simulation results show that the proposed network has successfully modeled the oxidation decomposition reaction process.

Multi-agent-based Fuzzy Dispatching for Trucks at Container Terminal

At container terminals, containers are transported from the marshalling yard to the quay and vice versa by Container Trucks (CTs). This study discusses how to dispatch CTs by utilizing information about pickup and delivery locations and time in future delivery tasks based on dynamic dispatching strategy in which multiple tasks are matched with multiple CTs. n this paper, Multi-agent system (MAS) is used as the basis for an intelligent dispatch system. To aim at that the characteristic of management of container terminal is how to optimize resource of terminal, the trends of decision-making way for management of container terminal, research and application of Multi-Agent system is summarized. Relationship between transport tasks and service of CTS has been taken as a contract net using the fuzzy set theory and method. Considering the load of communication and consultation efficiency in system, the bidirectional negotiation mechanism is adopted. The dispatching model based on Contract Network Protocol (CNP) using bidirectional negotiation is provided for assigning optimal delivery tasks to CTs and fuzzy reasoning process of dispatching decisions is suggested. The method has both virtues of precision of static planning and flexibility of CNP and has been confirmed by cases.

A feedback based CRI approach to fuzzy reasoning

Fuzzy reasoning methods are extensively used in intelligent systems and fuzzy control. In our previous work, an explicit feedback mechanism is embedded into optimal fuzzy reasoning methods, and the resulting fuzzy reasoning methods are more robust than the optimal fuzzy reasoning methods. An interesting question is that, without the introduction of optimization goals, can the robustness of fuzzy reasoning methods be improved by embedding feedback mechanisms? This paper is intended to answer the question. By embedding feedback mechanisms into CRI approach, a new feedback based CRI (FBCRI) approach is proposed and three implementation methods with different strategies are obtained. Simulation results show that the feedback mechanisms are really useful for the improvement of the robustness of CRI methods. At last, to test the applicability of the proposed approach, it is applied to the solution of a real-time path planning problem for UAVs. The effectiveness and efficiency of an FBCRI based real-time path planning algorithm are verified by representative test examples, which show that embedding feedback information into the fuzzy reasoning process actually improve the quality of flight paths.