Power quality time series data mining using S-transform and fuzzy expert system

A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing

In this paper, a fuzzy expert system is described for decision-making in geographical information systems (GIS), because fuzzy logic and expert system technology are available methods for approximating human reasoning and enhancing the level of intelligence in GIS. This system computes a potentiality map for problem solution with a problem specification provided by the user and expert knowledge (by means of production rules). It is a fuzzy expert system using a generalised modus ponens based on the fuzzy implication of Brouwer-Godel which is implemented by a fast algorithm. Several kind of rules are accepted and especially gradual rules. We have experimented our system on "Palni Hills", a mountainous region of meridional India to locate areas where the profitability of agriculture could be generally the best. The system is explicitly designed to provide the user with a decision-making environment for the cartographic problem solution to be carried out in a flexible manner.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The fuzzy-associative metaheuristic approach addresses the urgent task of developing a marketing decision support system based on a fuzzy trained associative rules expert system, aimed at improving the accuracy and efficiency of consumer preference analysis. The proposed system combines the interpretability of fuzzy logic with data-driven learning via associative rules and parameter identification using an adaptive multi-agent optimization method. To achieve this goal, associative rule learning techniques (Apriori and FP-Growth) were used to extract frequent consumer behavior patterns. A fuzzy expert system was developed, in which the parameters of membership functions are optimized by the Adaptive Vibrating Particle System (AVPS) metaheuristic. Unlike traditional vibrating particle systems, AVPS integrates iteration-dependent control of particle positions, enabling global search in early iterations and local refinement at later stages, thus improving convergence speed and solution precision. The architecture was implemented using Python-based tools (TensorFlow, Keras, Pandas, mlxtend, Scikit-Fuzzy), and validated on the “Consumer Behavior and Shopping Habits” dataset. The fuzzy expert system achieved an accuracy of 0.98, outperforming human experts (0.80), traditional VPS optimization (0.93), and backpropagation-based training (0.90). The system also reduces reliance on manually tuned parameters and increases robustness to data incompleteness and noise. Scientific novelty lies in combining a fuzzy associative rule-learning framework with AVPS-based optimization, offering a scalable and interpretable decision-making mechanism. The developed system contributes to the advancement of intelligent recommendation engines, personalized marketing tools, and decision support systems in consumer-oriented analytics.

Objectives: Operational effectiveness is an ultimate objective of any manufacturing organization. On the shop floor the main focus is on elimination of waste and delays. The main target is designing a product for which manufacturing is a low cost process. Once organizations select appropriate tools and methods, the decision on performance measures can be taken through which progress can be monitored. The present work offers fuzzy expert system for manufacturing method selection in a dynamic environment where the organization’s objectives are subject to constant amendments. Methods/ Statistical Analysis: In order to incorporate ambiguity into the manufacturing environment, the authorshave designed and developed a fuzzy expert system using Sugeno model based on the dynamic ranges of triangular membership functions for input objectives which is extremely efficient as compared to the Mamdani method in classifying vague data. In this paper, the authors proposearchitecture for fuzzy expert system based on multi objective criteria for selection of manufacturing method employing Sugeno model with constant output. Fuzzy expert system is created outside MATLAB and MATLAB is used only for creating user interface for querying methods based on objectives and for the evaluation of rules. Findings: As more than one manufacturing method may serve a single objective, each manufacturing method is encoded using a binary digit and the output is decimal representation of this binary encoding. Also, it is observed that, the number of fuzzy rules increase exponentially as a function of number of objectives. In order to cater to this problem, instead of generating so many rules the required modifications are performed at the code level to incorporate multi objective criteria. Finally a simulink model is developed for selective methods and objectives. The results obtained using Sugeno Fuzzy Expert System is compared with that obtained using Mamdani method and crisp expert system. Results: It is found that the crisp expert system and Sugeno type FIS yield similar results for classification while Mamdani type FIS offers more flexibility in method selection due to the nature of output members functions which are overlapping fuzzy sets. This yields the manager a greater freedom in method selection based on infrastructure and other resource availability. Keywords: Expert System, Fuzzy Logic, Manufacturing, MATLAB, Membership Functions, Simulink.

Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified. >

Troubleshooting of crude oil desalination plant using fuzzy expert system

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.

This paper aims to construct a cloud-based fuzzy expert system for the risk assessment of chronic kidney disease (CKD). The neural networks are firstly modelled for the detection of early chronic kidney disease while fuzzy rule-based expert system is employed for risk assessment. Firstly, three different neural network models are developed and experimented followed by the effectiveness measurement of three models based on the criteria of accuracy, sensitivity, and specificity among them. Secondly, the fuzzy rule-based expert system is developed and then is deployed to the cloud platform for conducting the self-risk assessment by the public users. In the ultimate goal, we anticipate that the expert system may also support CKD physicians an alternative to more accurately detect and assess the chronic kidney disease in its early stage of a patient. An online usefulness survey was also conducted. The result shows that there are 88.4% of all users responding to 'Helpful' based on our five-scale metrics. It was concluded that the system is helpful for a user to conduct a self-assessment on risk of kidney health status.

Great emphasis has been put on applying expert systems for transmission system fault diagnosis. However, very few papers deal with the unavoidable uncertainties that occur during operation involving the fault location and other available information. The paper proposes a method using fuzzy sets to cope with such uncertainties. A fuzzy expert system is developed, which requires much less memory to store an active database than that used by conventional rule-based expert systems. The fuzzy expert system identifies two basic network section sets, Shealthy for the healthy subnetwork and Sisland for the fault islands, using the postfault status of circuit breakers and relays. It then conducts inference to determine the most likely fault sections and the associated fault sequences. Two case studies are provided to demonstrate salient features of the proposed approach, and to compare results both with a conventional rule-based expert system and a refined genetic algorithm. The proposed fuzzy expert system can be operated either in a stand-alone manner, or as part of the overall architecture of a diagnosis system for parallel decision making.

In construction, many owners mitigate the risk of unforeseen contractor default by accepting only bonded contractors who must endure a rigorous evaluation process by surety brokers and surety underwriters. This evaluation process includes a financial analysis and a review of work on hand and past performance, all of which have reliable structured methods for their evaluation. Additionally, a number of subjective criteria are considered that are more difficult to capture and assess objectively but which can be modeled effectively using fuzzy logic. The purpose of this paper is to illustrate how fuzzy logic and expert systems can be combined to provide a structured approach to evaluating contractors for surety underwriting purposes. Fuzzy logic is used to model both the objective and subjective factors considered in contractor evaluation using linguistic terms, and expert rules are used to capture the surety experts’ reasoning process. A fuzzy expert system, SuretyAssist, is presented that can be used to provide an initial evaluation of general contractors as well as periodic reviews to determine whether or not to accept them as clients for bonding. SuretyAssist was validated using 31 actual cases of contractor evaluation and found to be accurate in 81% of the cases.

An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of an infant immediately after delivery. This can provide information on the health of the newborn infant and guide requirements for neonatal care, but there are problems with the technique. Samples frequently contain errors in one or more of the important parameters, preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret results. The development and validation of an expert system to overcome these difficulties is described. The initial development utilised conventional 'crisp' logic within the rule base and this system was evaluated to commercial release. This expert system validates the raw data, provides an interpretation of the results for clinicians and archives all the results, including the quality control and calibration data, for permanent storage. Subsequent development went on to incorporate fuzzy logic into part of the expert system knowledge base, but tests of this preliminary fuzzy system showed that it performed worse than the original crisp expert system. A tuning algorithm was then employed to modify the fuzzy model and this process resulted in improved performance to a level comparable to clinicians and superior to the crisp system. Finally, the entire knowledge base was converted to utilise fuzzy logic and this 'integrated' fuzzy expert system was validated against international expert opinion.

Proposes a fuzzy neural expert system (FNES) which has a feedforward fuzzy neural network whose input layer consists of fuzzy cell groups and crisp (non-fuzzy) cell groups. Here, the truthfulness of fuzzy information and crisp information of training data is represented by fuzzy cell groups and crisp cell groups, respectively. The expert system has the following two functions: generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; and extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (If-part) from a trained fuzzy neural network. The paper also gives a method to extract automatically fuzzy If-Then rules from the trained neural network. To prove the effectiveness and validity of the proposed fuzzy neural expert system, a fuzzy neural expert system for medical diagnosis has been developed. >

This paper describes a fuzzy expert system for design project performance evaluation and prediction. It presents a comprehensive framework of factors that impact design performance and factors used to measure performance. A new approach to generating membership functions based on objective data is presented. This approach provides for membership functions that are widely applicable in a given context and can be calibrated to suit different contexts. A method of generating expert rules to relate factors impacting design performance is presented. A survey was conducted to collect data to develop and test the proposed methods. These methods were used in developing the fuzzy expert system. Based on validation of the system, the fuzzy expert system provides accurate linguistic predictions of design performance parameters. The methods presented in this paper are useful and realistic in modeling design performance and in capturing the inherent subjectivity involved.Key words: construction, design, evaluation, expert systems, fuzzy logic, performance, prediction, productivity.

The techniques and tools developed for the assurance of reliability and quality in conventional software may also be applied to the development of fuzzy expert systems. This article addresses the use of one of the techniques and tools of software engineering in the development of fuzzy-rule-based expert systems. Coverage analysis applied to the rules of the knowledge base in a fuzzy-rule-based expert system can provide a measure towards assuring reliability and quality in soft expert systems. Coverage analysis is a technique used for verification and validation in software engineering. A description of verification and validation in the development of conventional software and the role of coverage analysis within those approaches to software quality assurance are first presented. Then the use of coverage analysis in the development of fuzzy-rule-based expert systems is examined. Experience from the application of coverage analysis to a knowledge base for a fuzzy-rule-based expert system is also presented.

This paper reports a new approach for providing intelligence in the expert system for diagnosis of diseases for rose flower. It describes the development of a web-based intelligent disease diagnosis system. This expert system is based on a fuzzy logic approach and the Euclidean distance method. This approach is based on rule advancement strategy. This approach enables the drawing of inferences with the enhanced intelligence. This approach is used in the existing fuzzy technique for inferencing in expert system. The proposed expert system incorporates new features: 1) development of knowledge management platform; 2) dynamic knowledge base creation strategy. The dynamically prompted rules are derived from those diagnosis sessions which resulted in successful decisions. This enables more efficient decision-making in the future sessions; 3) dynamic knowledge acquisition; 4) explanation facility which incorporates the rule firing history and rule explanation generator. This expert system gives an acceptable diagnosis of diseases. The inferences are drawn faster compared to traditional approaches. The proposed expert system which is based on rule advancement strategy has been tested for flower rose.