Application Of Fuzzy Expert System Research Articles

Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.

An intelligent approach for thermal-hydraulic studies on safety and efficiency of nuclear power plant

An effective identification of breakdowns is of premier importance for the safe and reliable operation of Nuclear Power Plants (NPP). The potential consequences of several terrorized activities may harm the society using nuclear and its associated facilities by using faulty components. Subsequently, rigorous nuclear reactor thermal-hydraulic experimental events support design improvement which are important vectors to be considered in nuclear safety measures. To challenge this problem, a number of linear and nonlinear pattern recognition methods have been utilized. Therefore, the aim of the present work is to study the application of fuzzy expert system (FES) model on a complex system such as nuclear reactor thermal-hydraulic system and their adaptation for the early forecast of irregular behaviour. The purpose of this study is to examine the relationship between reactor design parameters, in particular safety function areas of heat removal capability, conductivity of cladding, convective heat transfer characteristics of coolant and their impact of severity, and to illustrate how fuzzy expert system might play an important role in assessment of risk. Several simulation cases are conducted for evaluating the performance of the present diagnosis system. The simulation results show the good performance of the present intelligent approach in thermal-hydraulic studies on safety and efficiency of NPP.

Application of Fuzzy Expert System in Determination of MMPI-2 Protocol Code Type

Application of Fuzzy Expert System in Determination of MMPI-2 Protocol Code Type

Bearing faults diagnosis using fuzzy expert system relying on an Improved Range Overlaps and Similarity method

Bearing fault diagnosis represents the core of induction machines condition monitoring. This paper presents an application of fuzzy expert system (FES) to bearing faults diagnosis. Here, fuzzy rules are automatically induced from numerical data using the Similarity partition method. Data of faulty bearings presents high noise level. Thus, an Improved Range Overlaps method (IRO) is proposed to select input feature vectors by giving them validity degrees. The Similarity method partition was found confused with features presenting range overlap. Consequently, the new proposed Improved Range Overlaps method is found quite suitable for improving the classifier accuracy. The model validity and efficiency were proved using experimental bearing faults data from Case Western Reserve University database and the NSF I/UCR Center on Intelligent Maintenance Systems (IMS) database.

Application of Fuzzy Expert Systems to Manage the Projects Time in Iranian Gas Refineries

The National Iranian Gas Company (NIGC) is one of the top ten gas companies in the gas industry in the Middle East and is comprised of 7 gas refineries. So this company needs to apply the most optimum time management methods to achieve its goals. Custom scheduling calculation of project planning uses the Critical Path Method (CPM) as a tool for Planning Projects activities. CPM is now widely used in planning and managing projects but in spite of its wide application, this method has a critical weak point of not taking into account the uncertainties in scheduling calculation. This article aims to present a precise method based on the application of Fuzzy Expert Systems in order to improve the Time Estimation Method in construction projects and in this regard, reviews the results of the implementation of this method in construction projects of Iranian Gas Refineries. The results show that the proposed method increases the accuracy of time estimation about 7 to 22 percent.

Application of Fuzzy Expert Systems to Manage the Projects Time in Iranian Gas Refineries

Application of Fuzzy Expert Systems to Manage the Projects Time in Iranian Gas Refineries

Flight information system by using fuzzy expert inference

In this paper we explain some concept of fuzzy expert systems and its applications in business process. Moreover this article is written to be dedicated to the put of Esplan system in practice to gain information about flights and system of ticket services. The review and the analysis of information and expert system are given. The recognition of features of a research of data domain is carried out, and their informative values, as well as the properties are analyzed. Knowledge base is realized in environment expert system sell Esplan. The example of application of fuzzy expert system on base of Esplan for using in decision making problem is given. The paper also aims to develop the knowledge of rules to process the information about the decision making process in flight services information system.

Application of a fuzzy expert system for failure load estimation of two serial pinned or bolted sandwich composite plates

AbstractThe present paper reports about the application of fuzzy expert system for estimating failure loads of two serial pinned/bolted sandwich composite plates. Considered composite material for present application was produced by a glass fiber reinforced layer and aluminum sheets to create sandwich structures. Briefly outlines, the experimental data of a previous study were related to different geometrical boundary conditions and also numerous preload moments as 0 (pinned), 2, 3, 4 and 5 (bolted) Nm. Anyway, both a fuzzy expert system and a regression analysis were applied depending on mentioned geometrical parameters and pinned/bolted joint arrangements in this work. Obtained results point out that the fuzzy expert system was more suitable than regression analysis method for modeling and estimation of analyzed sandwich composite. Achievements of the fuzzy expert system and the regression analysis methods were considered in terms of error ratios and mean absolute deviations.

Application of Fuzzy Expert Systems in Assessing Risk Management in the US Army

A risk management process is most effective when the users are properly educated on the process and the process itself promotes a uniform perception of risk. Every soldier in the US Army is expected to be capable of managing risk— eliminating it when possible or mitigating it to an acceptable level through the principles and application a formal, multistep, iterative process known as the US Army Risk Management program. This paper describes a study in which the researchers developed and used a fuzzy rule based expert system to evaluate a respondent population’s ability to assess risk using the US Army’s Risk Management program, and to determine if there were any common characteristics amongst those respondents with similar output. The results showed that while some factors such as active duty versus reserve status yielded negligible differences, there existed a significant difference between the way the commissioned and noncommissioned officer participants perceived risk. This information is one key to understanding that the differences in the way commissioned and non-commissioned officers are taught the Risk Management process and how it can affect their perceptions of risk and suggests that a better, more uniform method of risk training could be developed for the training audiences.

Application of Fuzzy Expert System for Cucumber Growing Irrigation

Application of Fuzzy Expert System for Cucumber Growing Irrigation

Remedy Selection based on Artificial Intelligent Methods

Homeopathy decision is a decision under uncertainty and an essential activity for accurate treatment. Expert systems have long history of application in medical diagnosis. In this paper we study application of Fuzzy Expert System and decision tree for selection of suitable remedy in Homeopathy. A decision made by Homeopath is highly dependent on the quality of selected rubrics. The rubrics are collected during case taking and generally contains expressions that might be considered fuzzy, such as 'never', 'sometimes', 'always', and so on, making it difficult to model them with conventional computational methods. In this context, fuzzy set theory in expert systems is an interesting tool to deal with the representation of inaccurate medical entities. Hence, we can go from natural language (linguistic variables) to numerical variables which are more convenient to handle in a computer. The proposed method reduces sensitivity of system to homeopath mistakes and increase security of system.

An Application of Fuzzy Expert Systems to Strategic Investments: The Case of Florim S.P.A.

This paper makes use of a fuzzy expert system to evaluate a strategic investment. In particular, the model proposed aims at replicating the actual decision process accomplished by Florim S.p.a., an Italian ceramic tile firm which recently had the opportunity of buying a firm in the USA. The model is perfectly consistent with the evaluation process conducted by Florim’s experts and on the basis of the same data available to the expert’s panel, our expert system provides the same investment value and therefore the same solution to the decision process.

Application of fuzzy expert system to estimate dimensional errors of forging products having complicated shape

Abstract Measurement system that uses mechanical linear displacement sensor and lab view software has been developed to inspect the dimensions of forging products having complicated shape. The measurement system is designed specifically to measure the dimensions of forging products having complicated shape, such as forged outer race of constant velocity (CV) joint having six ball grooves. Newly developed system provides high measurement accuracy with a simple operational sequence using fuzzy inference system. During the actual forging operation, several types of dimensional errors may occur due to the inadequate control of process parameters. Therefore, the effect of process parameters on the occurrence of specific dimensional error must be known for the precise control of forging operation. Therefore, an efficient method to predict possible dimensional error of CV joint from given process parameter has been developed by using a fuzzy inference system.

Motion tracking and prediction using fuzzy logic

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Predicting Industrial Construction Labor Productivity Using Fuzzy Expert Systems

The objective of this technical note is to illustrate the application of fuzzy expert systems to the modeling of a practical problem—that of predicting the labor productivity of two common industrial construction activities: rigging pipe and welding pipe. This note illustrates how to develop and test such a model, given the realistic constraints of subjective assessments, multiple contributing factors, and limitations on data sets. The factors that affect the productivity of each activity are identified, and fuzzy membership functions and expert rules are developed. The models are validated using data collected from an actual construction project. The resulting models are found to have high linguistic prediction accuracies. This note is of relevance to researchers by demonstrating how a fuzzy expert system can be developed and tested. It is of relevance to industry practitioners by illustrating how fuzzy logic and expert systems modeling can be exploited to help them solve real world problems.

Application of fuzzy expert systems in assessing operational risk of software

Risk is the potential for realization of undesirable consequences of an event. Operational risk of software is the likelihood of untoward events occurring during operations due to software failures. NASA IV&V Facility is an independent institution which conducts Independent Assessments for various NASA projects. Its responsibilities, among others, include the assessments of operational risks of software. In this study, we investigate Independent Assessments that are conducted very early in the software development life cycle. Existing risk assessment methods are largely based on checklists and analysis of a risk matrix, in which risk factors are scored according to their influence on the potential operational risk. These scores are then arithmetically aggregated into an overall risk score. However, only incomplete project information is available during the very early phases of the software life cycle, and thus, a quantitative method, such as a risk matrix, must make arbitrary assumptions to assess operational risk. We have developed a fuzzy expert system, called the Research Prototype Early Assessment System, to support Independent Assessments of projects during the very early phases of the software life cycle. Fuzzy logic provides a convenient way to represent linguistic variables, subjective probability, and ordinal categories. To represent risk, subjective probability is a better way than quantitative objective probability of failure. Furthermore, fuzzy severity categories are more credible than numeric scores. We illustrated how fuzzy expert systems can infer useful results by using the limited facts about a current project, and rules about software development. This approach can be extended to add planned IV&V level, history of past NASA projects, and rules from NASA experts.