A Fuzzy Reasoning System and Its Heuristic Inference Algorithm

Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.

Mathematical Foundation of Basic Algorithms of Fuzzy Reasoning

Algorithm of fuzzy reasoning has been successful applied in fuzzy control,but its theoretical foundation of algorithms has not been thoroughly investigated. In this paper,structure of basic algorithms of fuzzy reasoning was studied, its rationality was discussed from the viewpoint of logic and mathematics, and three theorems were proved. These theorems shows that there always exists a mathe-~matical relation (that is, a bounded real function) between the premises and the conclusion for fuzzy reasoning, and in fact various algorithms of fuzzy reasoning are specific forms of this function. Thus these results show that algorithms of fuzzy reasoning are theoretically reliable.

A Semantic Retrieval Method Based on the Fuzzy Reasoning

This paper gives a semantic fuzzy retrieval method of multimedia object, discusses the principle of fuzzy semantic retrieval technique, presents a fuzzy reasoning mechanism based on the knowledge base, and designs the relevant reasoning algorithms. Researchful results have innovative significance.

Fuzzy multi-criteria decision-making approach with incomplete information based on evidential reasoning

The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

Universal triple I fuzzy reasoning algorithm of function model based on quotient space

Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design, the theory of quotient space and universal triple I fuzzy reasoning method are introduced, and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed. Firstly, the product function granular model based on the quotient space theory is built, with its function granular representation and computing rules defined at the same time. Secondly, in order to quickly achieve function granular model from function requirement, the function modeling method based on universal triple I fuzzy reasoning is put forward. Within the fuzzy reasoning of universal triple I method, the small-distance-activating method is proposed as the kernel of fuzzy reasoning; how to change function requirements to fuzzy ones, fuzzy computing methods, and strategy of fuzzy reasoning are respectively investigated as well; the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved. Lastly, the validity of the function granular model and function modeling algorithm is validated. Through our method, the reasonable function granular model can be quickly achieved from function requirements, and the fuzzy character of conceptual design can be well handled, which greatly improves conceptual design.

Fuzzy Reasoning Methods by Choosing Different Fuzzy Counters and Analysis of Effect

Different fuzzy reasoning methods were gave by choosing different fuzzy counters.This article generally introduced the basic structure of fuzzy controller,and compared and analysised the reasoning effect of fuzzy reasoning methods and the effect of computer simulating control basicly on different fuzzy counters.

A NOVEL TEMPORAL ERROR CONCEALMENT METHOD BASED ON FUZZY REASONING FOR H.264

In this paper,a fuzzy reasoning based temporal error concealment method is proposed. The basic temporal error concealment is implemented by estimating Motion Vector (MV) of the lost MacroBlock (MB) from its neighboring MVs. Which MV is the most proper one is evaluated by some criteria. Generally,two criteria are widely used,namely Side Match Distortion (SMD) and Sum of Absolute Difference (SAD) of corresponding MV. However,each criterion could only partly describe the status of lost block. To accomplish the judgement more accurately,the two measures are considered together. Thus a refined measure based on fuzzy reasoning is adopted to balance the effects of SMD and SAD. Terms SMD and SAD are regarded as fuzzy input and the term ‘similarity’ as output to complete fuzzy reasoning. Result of fuzzy reasoning represents how the tested MV is similar to the original one. And k-means clustering technique is performed to define the membership function of input fuzzy sets adaptively. According to the experimental results,the concealment based on new measure achieves better performance.

Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System

The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

Fuzzy Consistent Matrix and Its Application

This paper presents a kind of fuzzy consistent matrix and introduces its application in analytic hierarchy process and adaptive fuzzy reasoning.

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care environments.Pervasive health care monitoring system can be a potential alternative,especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care,mobile care and home care.In this aspect,we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation.It facilitates better healthcare assistance,especially for COVID’19 patients and quarantined people.It identies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model.Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identication.Linguistics rules are framed based on the fuzzy set attributes belong to different context types.The fuzzy semantic rules are used to identify the relationship among the attributes,and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation.Outcomes are measured using a fuzzy logic-based context reasoning system under simulation.The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

Fuzzy-Immune PID Control for AMB Systems

In order to improve the dynamic performance of active magnetic hearing systems with highly nonlinear and naturally unstable dynamics, a new nonlinear fuzzy-immune proportional integral-derivative （PID） controller is proposed by combining the immune feedback law with linear PID con trol. This controller consists of a PID controller and a basic immune proportional controller in cascaded connection, the nonlinear function of the immune proportional controller is realized by using fuzzy reasoning. Simulation results demon strate that the active magnetic bearing system with the proposed controller has better dynamic performance and disturbance rejection ability than using the linear PID controller.

A Wavelet-Based Fuzzy Neural Network for Interpolation of Fuzzy If-Then Rules

In this paper, a wavelet-based fuzzy neural network with its structure and a learningalgorithm is proposed and the simulation results are given to prove its feasibility.

Dynamic character analysis for the arc welding power source based on fuzzy logic

A lot of experimental methods have been brought forth to assess the dynamic character of the arc welding power source, but up to now, this issue has not been solved very well. In this paper, based on the fuzzy logic reasoning method, a dynamic character assessing model for the arc welding power source was established and used to analyze the dynamic character of the welding power source. Three different types of welding machine have been tested, and the characteristic information of the electrical signals such as re-striking arc voltage, low welding current and so on of the welding process were extracted accurately by using a self-developed welding dynamic arc wavelet analyzer. The experimental results indicate that this model can be used as a new assessing method for the dynamic character of the arc welding power source.

Designing Genetic Regulatory Networks Using Fuzzy Petri Nets Approach

In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition （HFT） to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.

Method of Dynamic Knowledge Representation and Learning Based on Fuzzy Petri Nets

A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.

Majorizational Choosing of Several Different Fuzzy Counter Operator

Different fuzzy reasoning methods were made by choosing different fuzzy operater.This article generally introduced the basic structure of fuzzy controller,and gave several different fuzzy controllers,and compared and analyzed different fuzzy counters in theory and computer simulating control and realized majorizational choosing of several fuzzy counters.

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

In the medical field,the detection of breast cancer may be a mysterious task.Physicians must deduce a conclusion from a significantly vague knowledge base.A mammogram can offer early diagnosis at a low cost if the breasts'satisfactory mammogram images are analyzed.A multi-decision Intuitionistic Fuzzy Evidential Reasoning(IFER)approach is introduced in this paper to deal with imprecise mammogram classification efficiently.The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy.The results of the proposed technique are approved through simulation.The simulation is created utilizing MATLAB software.The screening results are classified and finally grouped into three categories:normal,malignant,and benign.Simulation results show that this IFER method performs classification with accuracy almost 95%compared to the already existing algorithms.The IFER mammography provides high accuracy in providing early diagnosis,and it is a convenient diagnostic tool for physicians.

Differently implicational α-universal triple I restriction method of (1, 2, 2) type

From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.