Fuzzy Expert System to Characterize Students
Van Hecke, T.
2011-01-01
Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…
Fuzzy Expert System to Characterize Students
Van Hecke, T.
2011-01-01
Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…
Yarn Strength Modelling Using Genetic Fuzzy Expert System
Banerjee, Debamalya; Ghosh, Anindya; Das, Subhasis
2013-05-01
This paper deals with the modelling of cotton yarn strength using genetic fuzzy expert system. Primarily a fuzzy expert system has been developed to model the cotton yarn strength from the constituent fibre parameters such as fibre strength, upper half mean length, fibre fineness and short fibre content. A binary coded genetic algorithm has been used to improve the prediction performance of the fuzzy expert system. The experimental validation confirms that the genetic fuzzy expert system has significantly better prediction accuracy and consistency than that of the fuzzy expert system.
A fuzzy expert system for diabetes decision support application.
Lee, Chang-Shing; Wang, Mei-Hui
2011-02-01
An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.
Fuzzy Expert System for Heart Attack Diagnosis
Hassan, Norlida; Arbaiy, Nureize; Shah, Noor Aziyan Ahmad; Afizah Afif@Afip, Zehan
2017-08-01
Heart attack is one of the serious illnesses and reported as the main killer disease. Early prevention is significant to reduce the risk of having the disease. The prevention efforts can be strengthen through awareness and education about risk factor and healthy lifestyle. Therefore the knowledge dissemination is needed to play role in order to distribute and educate public in health care management and disease prevention. Since the knowledge dissemination in medical is important, there is a need to develop a knowledge based system that can emulate human intelligence to assist decision making process. Thereby, this study utilized hybrid artificial intelligence (AI) techniques to develop a Fuzzy Expert System for Diagnosing Heart Attack Disease (HAD). This system integrates fuzzy logic with expert system, which helps the medical practitioner and people to predict the risk and as well as diagnosing heart attack based on given symptom. The development of HAD is expected not only providing expert knowledge but potentially become one of learning resources to help citizens to develop awareness about heart-healthy lifestyle.
Yarn Strength Modelling Using Fuzzy Expert System
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Abhijit Majumdar, Ph.D.
2008-12-01
Full Text Available Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.
Fuzzy expert system for diagnosing diabetic neuropathy.
Rahmani Katigari, Meysam; Ayatollahi, Haleh; Malek, Mojtaba; Kamkar Haghighi, Mehran
2017-02-15
To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy. The research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists' perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system. The final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%). The system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients.
Fuzzy expert system for diagnosing diabetic neuropathy
Rahmani Katigari, Meysam; Ayatollahi, Haleh; Malek, Mojtaba; Kamkar Haghighi, Mehran
2017-01-01
AIM To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy. METHODS The research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists’ perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system. RESULTS The final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%). CONCLUSION The system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients. PMID:28265346
PERFORMANCE EVALUATION OF PENSION FUNDS WITH FUZZY EXPERT SYSTEM
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SERDAR KORUKOĞLU
2013-06-01
Full Text Available Financial rating and ranking firms often use linguistic instead of numerical values. When input data are mostly qualitative and are based on subjective knowledge of experts, the Fuzzy Set Theory provides a solid mathematical model to represent and handle these data. The aim of this study is developing a fuzzy expert model to evaluate the performance of the pension funds by using their risk and return values. The method is used for evaluating the performance of the randomly selected of twenty seven Turkish pension funds. The obtained results proved that the fuzzy expert system is appropriate and consistent for performance evaluation.
UNCERTAIN KNOWLEDGE MANAGEMENT IN EXPERT SYSTEMS USING FUZZY METAGRAPHS
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper presented a new graph-theoretic construct fuzzy metagraphs and discussed their applications in constructing--fuzzy knowledge base. Fuzzy metagraphs describe the relationships between sets of fuzzy elements but not single fuzzy element and offer some distinct advantages both for visualization of systems, as well as for formal analysis of system structure. In rule-based system, a fuzzy metagraph is a unity of the knowledge base and the reasoning engine. Based on the closure of the adjacency matrix of fuzzy metagraphs, this paper presented an optimized inferential mechanism working mainly by an off-line approach. It can greatly increase the efficiency of inference. Finally, it was applied in a daignostic expert system and satisfactory results were obtained.
Research and Design of a Fuzzy Neural Expert System
Institute of Scientific and Technical Information of China (English)
王仕军; 王树林
1995-01-01
We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.
Fault detection thermal storage system by expert system using fuzzy abduction
Energy Technology Data Exchange (ETDEWEB)
Yamada, Koichi [Yamatake-Honeywell Co., Ltd, Yokohama (Japan). Advanced Technology Center; Kamimura, Kazuyuki [Yamatake-Honeywell Co., Ltd., Tokyo (Japan). Building Systems Div.
1996-12-31
Fuzzy abduction is a procedure for deriving fuzzy sets of hypotheses which explain a given fuzzy set of events using a set of rules with a truth value. The derived fuzzy sets of hypotheses are called fuzzy explanations. This presentation starts with discussion about diagnosis using conventional expert systems and that using fuzzy relational equations. Then, it proposes a new approach using a fuzzy abduction, and applies the technique to fault detection of a thermal storage system. (orig.)
Diagnosa Gangguan Perkembangan Anak Dengan Metode Fuzzy Expert System
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Diki Arisandi
2017-05-01
Full Text Available AbstrakAnak-anak dibawah umur 10 tahun merupakan fase yang sangat perlu diperhatikan perkembangannya oleh orang tua dan dibantu oleh pakar, apakah mengalami gangguan perkembangan atau tidak. Gangguan perkembangan anak dapat didiagnosis dari perilaku yang diperlihatkan oleh anak dengan cara observasi oleh seorang pakar psikologi anak. Hasil diagnosa dari observasi yang dilakukan beberapa pakar bisa saja berbeda. Hal ini membuat para orang tua menjadi kebingungan terhadap tindak lanjut yang harus dilakukan kepada anak mereka. Untuk mempermudah mendiagnosis gangguan perkembangan pada anak perlu adanya sebuah sistem pakar berbasis Fuzzy. Metode Fuzzy yang diterapkan didasari atas rentang logika berpikir manusia seperti dingin dan panas, tinggi dan rendah, dan lainnya. Diharapkan dengan adanya sistem pakar berbasis fuzzy ini, hasil diagnosa dapat menghasilkan solusi seperti nalar manusia dari sehingga didapatkan solusi untuk tindak lanjut pada gangguan anak. Kata kunci: Diagnosa, Fuzzy, Fungsi Keanggotaan, Gangguan perkembangan, Sistem Pakar. AbstractChildren under 10 years is a critical phase of their developmental and should be noticed by parents and assisted by experts, whether experiencing developmental disruption or not. Children developmental disruption can be diagnosed from behaviors shown by children by observation by a psychologist. Diagnosis results from observations made by some experts may be different. This makes the parents become confused about the follow-up to be done to their children. A Fuzzy-based expert system is needed to overcome the children developmental disruption. The applied Fuzzy method is based on the logical range of human thinking such as cold and hot, high and low, and others. With the fuzzy-based expert system, the diagnostic results can produce solutions such as human reasoning from that obtained a solution to following up on children disruption. Keywords: Diagnosis, Fuzzy, Membership Function, Developmental
FHESMM: Fuzzy Hybrid Expert System for Marketing Mix Model
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Mehdi Neshat
2011-11-01
Full Text Available Increasing customers satisfaction in this developed world is the most important factor to have a successful trade and production. New marketing methods and supervising the marketing choices will have a key role to increase the profit of a company. This paper investigates an expert system through four main principles of marketing (price, product, Place and Promotion and their composition with a logic fuzzy system and benefiting from the experiences of marketing specialists. Comparing with the other systems, this one has special properties such as investigating and extracting different fields in which affect the customers satisfaction directly or indirectly as input parameters (26, using knowledge of experts to design inference system rule, composing the results of five fuzzy expert systems and calculating final result(customers satisfaction and finally creating a high function expert system on management and guiding the managers to do a successful marketing in dynamic markets.
Towards a Fuzzy Expert System on Toxicological Data Quality Assessment.
Yang, Longzhi; Neagu, Daniel; Cronin, Mark T D; Hewitt, Mark; Enoch, Steven J; Madden, Judith C; Przybylak, Katarzyna
2013-01-01
Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that "five toxicologists may have six opinions". In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation
Improving Computer Based Speech Therapy Using a Fuzzy Expert System
Ovidiu Andrei Schipor; Stefan Gheorghe Pentiuc; Maria Doina Schipor
2012-01-01
In this paper we present our work about Computer Based Speech Therapy systems optimization. We focus especially on using a fuzzy expert system in order to determine specific parameters of personalized therapy, i.e. the number, length and content of training sessions. The efficiency of this new approach was tested during an experiment performed with our CBST, named LOGOMON.
A Novel Web-based Human Advisor Fuzzy Expert System
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Vahid Rafe
2013-02-01
Full Text Available The applications of the Internet-based technologies and the concepts of fuzzy expert systems (FES have created new methods for sharing and distributing knowledge. However, there has been a general lack of investigation in the area of web-based fuzzy expert systems. In this paper, the issues associated with the design, development, and use of web-based applications from a standpoint of the benefits and challenges of development and utilization are investigated. The original theory and concepts in conventional FES are reviewed and a knowledge engineering framework for developing such systems is revised. For a human advisor to have a satisfying performance, expertise is a must. In addition, some of advisory rules are subject to change because of domain knowledge update. The human requests may have linguistic or crisp forms and a conventional expert system (ES is not able to overcome the fuzziness in the problem nature. In this research, a Web-based fuzzy expert system for Common Human Advisor (FES-CHA is developed and implemented to be used as a student advisor at the department‘s web portal. The system is implemented by using Microsoft Visual Studio .NET 2010, MVC and Microsoft SQL Server 2012.
Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System
Institute of Scientific and Technical Information of China (English)
吴顺祥; 倪子伟; 李茂青
2002-01-01
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 the 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.
Applications of fuzzy sets to rule-based expert system development
Lea, Robert N.
1989-01-01
Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.
Human Disease Diagnosis Using a Fuzzy Expert System
Hasan, Mir Anamul; Chowdhury, Ahsan Raja
2010-01-01
Human disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based expert system dealing with human disease diagnosis has to overcome various difficulties. This paper describes a project work aiming to develop a web-based fuzzy expert system for diagnosing human diseases. Now a days fuzzy systems are being used successfully in an increasing number of application areas; they use linguistic rules to describe systems. This research project focuses on the research and development of a web-based clinical tool designed to improve the quality of the exchange of health information between health care professionals and patients. Practitioners can also use this web-based tool to corroborate diagnosis. The proposed system is experimented on various scenarios in order to evaluate it's performance. In all the cases, proposed system exhibits satisfactory results.
A Novel Web-based Human Advisor Fuzzy Expert System
Directory of Open Access Journals (Sweden)
Vahid Rafe
2013-01-01
Full Text Available The applications of the Internet-based technologies and the concepts of fuzzy expert systems (FES have creatednew methods for sharing and distributing knowledge. However, there has been a general lack of investigation in thearea of web-based fuzzy expert systems. In this paper, the issues associated with the design, development, and useof web-based applications from a standpoint of the benefits and challenges of development and utilization areinvestigated. The original theory and concepts in conventional FES are reviewed and a knowledge engineeringframework for developing such systems is revised. For a human advisor to have a satisfying performance, expertise isa must. In addition, some of advisory rules are subject to change because of domain knowledge update. The humanrequests may have linguistic or crisp forms and a conventional expert system (ES is not able to overcome thefuzziness in the problem nature. In this research, a Web-based fuzzy expert system for Common Human Advisor(FES-CHA is developed and implemented to be used as a student advisor at the department's web portal. Thesystem is implemented by using Microsoft Visual Studio .NET 2010, MVC and Microsoft SQL Server 2012.
Revamping Grooving Process for Sustainability using Fuzzy Expert System
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Iqba Asif
2016-01-01
Full Text Available The article presents an application of a fuzzy expert system for renovating a metal cutting process to cope with the sustainability requirements. The work seeks a sustainable balance between energy consumption, productivity and tool damage. Cylindrical grooving experiments were performed to generate data related to quantification of the effects of material hardness, cutting speed, width of cut and feed rate on the aforementioned sustainability measures. A fuzzy knowledge-base was developed that suggests the most suitable adjustments of the controlled variables that would lead to achievement of various combinations of the objectives.
Designing Fuzzy Rule Based Expert System for Cyber Security
Goztepe, Kerim
2016-01-01
The state of cyber security has begun to attract more attention and interest outside the community of computer security experts. Cyber security is not a single problem, but rather a group of highly different problems involving different sets of threats. Fuzzy Rule based system for cyber security is a system consists of a rule depository and a mechanism for accessing and running the rules. The depository is usually constructed with a collection of related rule sets. The aim of this study is to...
A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital
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Mohammad Hossein Fazel Zarandi
2012-01-01
Full Text Available A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.
Expert,Neural and Fuzzy Systems in Process Planning
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
Computer aided process planning (CAPP) aims at improving efficiency, quali t y, and productivity in a manufacturing concern through reducing lead-times and costs by utilizing better manufacturing practices thus improving competitiveness in the market. CAPP attempts to capture the thoughts and methods of the experie nced process planner. Variant systems are understandable, generative systems can plan new parts. Expert systems increase flexibility, fuzzy logic captures vague knowledge while neural networks learn. The combination of fuzzy, neural and exp ert system technologies is necessary to capture and utilize the process planning logic. A system that maintains the dependability and clarity of variant systems , is capable of planning new parts, and improves itself through learning is neede d by industry.
ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic
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P.Chinniah
2009-12-01
Full Text Available Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elder’s algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease. Keywords -Fuzzy clustering, symptoms, fuzzy severity scale, weight factor, Minkowski distance, ICD, WHO, Rules Base, TSQL
ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic
Chinniah, P
2010-01-01
Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elders algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease.
Contribution of a fuzzy expert system to regulatory impact analysis
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Marco Antônio da Cunha
2015-09-01
Full Text Available Regulatory Impact Analysis (RIA has been consolidating in Brazilian regulatory agencies throughout the last decades. The RIA methodology aims to examine the regulatory process, measure the costs and benefits generated, as well as other effects of social, political or economic nature caused by a new or an existing regulation. By analysing each regulatory option, the expert or regulator faces a myriad of variables, usually of qualitative nature, that are difficult to measure and with a high degree of uncertainty. This research complements the existing literature, given the scarcity of decision support models in RIA that – regardless of the problem treated – incorporate the tacit knowledge of the regulation expert. This paper proposes an exploratory approach using a Fuzzy Expert System, which therefore helps to enrich the decision process in the final stage of comparison of the regulatory options.
Bank Customer Credit Scoring by Using Fuzzy Expert System
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Ali Bazmara
2014-10-01
Full Text Available Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.
Parkinson's disease Assessment using Fuzzy Expert System and Nonlinear Dynamics
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GEMAN, O.
2013-02-01
Full Text Available This paper proposes a new screening system for quantitative evaluation and analysis, designed for the early stage detection of Parkinson disease. This has been carried out in the view of improving the diagnosis currently established upon a basis of subjective scores. Parkinson?s disease (PD appears as a result of dopamine loss, a chemical mediator that is responsible for the body?s ability to control movements. The symptoms reflect the loss of nerve cells, due to an unknown. The input parameters of the system are represented by amplitude, frequency, the spectral characteristic and trembling localization. The main symptoms include trembling of hand, arms, movement difficulties, postural instability, disturbance of coordination and equilibrium, sleep disturbance, difficulties in speaking, reducing of voice volume. The medical knowledge in PD field is characterized by imprecision, uncertainty and vagueness. The proposed system (fuzzy expert systems is non-invasive and, easy to use by both physicians and patients at home.
Life insurance risk assessment using a fuzzy logic expert system
Carreno, Luis A.; Steel, Roy A.
1992-01-01
In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.
Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert systemand build up intelligent fault diagnosis for a type of mis-sile weapon system, the concrete implementation of a fuzzyNN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, theintelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment.The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosisfor large-scale missile weapon equipment.
Designing a fuzzy expert system for selecting knowledge management strategy
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Ameneh Khadivar
2014-12-01
Full Text Available knowledge management strategy is mentioned as one of the most important success factors for implementing knowledge management. The KM strategy selection is a complex decision that requires consideration of several factors. For evaluation and selection of an appropriate knowledge management strategy in organizations, many factors must be considered. The identified factors and their impact on knowledge management strategy are inherently ambiguous. In this study, an overview of theoretical foundations of research regarding the different knowledge management strategies has been done And factors influencing the knowledge management strategy selection have been extracted from conceptual frameworks and models. How these factors influence the knowledge management strategy selection is extracted through the fuzzy Delphi. Next a fuzzy expert system for the selection of appropriate knowledge management strategy is designed with respect to factors that have an impact on knowledge management strategy. The factors which influence the selection of knowledge management strategy include: general business strategy, organizational structure, cultural factors, IT strategy, strategic human resource management, social level, the types of knowledge creation processes and release it. The factors which influence the knowledge management strategy selection include: business strategy general, organizational structure, cultural factors, IT strategy, human resource management strategies, socialization level, knowledge types and its creation and diffusion processes. According to identified factors which affect the knowledge management strategy, the final strategy is recommended based on the range of human-oriented and system-oriented by keep the balance of explicit and implicit knowledge. The Designed system performance is tested and evaluated by the information related to three Iranian organization.
Fuzzy logic applications to expert systems and control
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology
Bonissone, Piero P.
1995-06-01
We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.
Energy Technology Data Exchange (ETDEWEB)
Averkin, A.A. [Russian Academy of Sciences, Moscow (Russian Federation). Computer centre
1994-12-31
A new type of fuzzy expert system for assisting the operator`s decisions in nuclear power plant system in non-standard situations is proposed. This expert system is based on new approaches to fuzzy logics acquisition and to fuzzy logics testing. Fuzzy logics can be generated by a T-norms axiomatic system to choose the most suitable to operator`s way of thinking. Then the chosen fuzzy logic is tested by simulation of inference process in expert system. The designed logic is the input of inference module of expert system.
Directory of Open Access Journals (Sweden)
P. Kalyana Sundaram
2016-11-01
Full Text Available The paper presents a novel method for the assessment of the power quality disturbances in the distribution system using the Kalman filter and fuzzy expert system. In this method the various classes of disturbance signals are developed through the Matlab Simulink on the test system model. The characteristic features of the disturbance signals are extracted based on the Kalman filter technique. The obtained features such as amplitude and slope are given as the two inputs to the fuzzy expert system. It applied some rules on these inputs to assess the various PQ disturbances. Fuzzy classifier has been carried out and tested for various power quality disturbances. The results clearly demonstrate that the proposed method in the distribution system has the ability to detect and classify PQ events.
Designing a Fuzzy Expert System For Measuring E-Banking Service Quality
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Shohreh Nasri Nasrabadi
2015-06-01
Full Text Available With dramatic development of information technology and its widespread applications, increasing dependence on information technology and the increasing complexity of technologies and services which used in organizations, the management of these technologies and services is more difficult. Hence,wiith the development of IT-based banking services,including electronic banking services,the requirement methods which evaluate the quality of these services in the organization has been increased. Therefore, in this study, a comprehensive model for more accurate measurement of e-banking service quality through an extensive literature review and questions from experts in this field is presented using Fuzzy Delphi Technique. In this regard,after the identification of relevant dimensions and criteria, since the traditional scale can not accurately evaluate the e-banking service quality in the terms of the uncertainty , these dimensions and criteria are to be fuzzified,then the fuzzy expert system conceptual model is presented.Finally, a fuzzy expert system with 5 fuzzy module and a graphical user interface is provided in MATLAB.The fuzzy expert system indicate the final status of e-banking service quality and the main dimension.In this paper, the application of this system has been has been examined in the Sina bank. Finally by calculating expert system errors, optimum performance of designed fuzzy system has been approved which is evaluated by sina bank information.
The matrix representation of fuzzy knowledge and its application to the expert systems design
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V. Levchenko
1993-02-01
Full Text Available An approach to the diagnostic type expert systems design based on the special matrix representation of fuzzy predicates in the tribute model of the problem domain is presented. Intensive representation of predicates by means of sectional matrices is an analogue of the conjunctive normal form. Rules, positive examples and negative examples (in general, all fuzzy can be used to form knowledge base. Diagnostics problem is thought of as finding some attribute values provided that the information about other attribute values is available. Logical inference is based on an equivalent transformation of the matrix to that containing all prime disjuncts by using the operation of fuzzy resolution . Two strategies to carry out such transformation are described. On the basis of formalism presented the expert system shell EDIP is developed, the first version of that is non-fuzzy and the second one allows working with fuzzy data and conclusions.
Fuzzy logic based expert system for the treatment of mobile tooth.
Mago, Vijay Kumar; Mago, Anjali; Sharma, Poonam; Mago, Jagmohan
2011-01-01
The aim of this research work is to design an expert system to assist dentist in treating the mobile tooth. There is lack of consistency among dentists in choosing the treatment plan. Moreover, there is no expert system currently available to verify and support such decision making in dentistry. A Fuzzy Logic based expert system has been designed to accept imprecise and vague values of dental sign-symptoms related to mobile tooth and the system suggests treatment plan(s). The comparison of predictions made by the system with those of the dentist is conducted. Chi-square Test of homogeneity is conducted and it is found that the system is capable of predicting accurate results. With this system, dentist feels more confident while planning the treatment of mobile tooth as he can verify his decision with the expert system. The authors also argue that Fuzzy Logic provides an appropriate mechanism to handle imprecise values of dental domain.
A fuzzy expert system for the integration of collaborative sypply chains
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Bautista-Santos, Horacio
2016-08-01
Full Text Available The integration of supply chains has become a global operations strategy for many organisations because it allows them to improve customer service, minimise operating costs, and synchronise channels across the supply chain. This paper presents the design and implementation of a fuzzy expert system for the integration of collaborative supply chains. A measurement instrument that was statistically validated and formulated using a mathematical model was designed to implement the fuzzy expert system. This instrument was applied in 44 small-, medium-, and large-sized Mexican enterprises to determine their integration level. Specific actions were proposed, based on the results, to improve the attained integration level.
Model Based Fuzzy Expert System for Measuring Organization Knowledge Management
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Houshang Taghizadeh
2012-02-01
Full Text Available This paper presents a model based on fuzzy set theory for determining the score of knowledge management in organization. The introduced model has five stages. In the first stage, input and output variable of model are characterized by available theories. Inputs are as follows: knowledge acquisition, knowledge storage, knowledge creation, knowledge sharing and knowledge transfer. The output is as follow score of knowledge management in organization. In the second stage, the input and output are converted into fuzzy numbers after classification. Inference rules are explained in the third stage. In the fourth stage, defuzzification is performed, and in the fifth stage, the devised system is tested. The test result shows that the presented model has high validity. Ultimately, by using the designed model, the score of knowledge management for Tabriz Kar machinery industry was calculated. The statistical population consists of 50 members of this organization. All the population has been studied. A questionnaire was devised, and its validity and reliability were confirmed. The result indicated that the score of knowledge management in Tabriz Kar machinery industry with the membership rank of 0.924 was at an average level and with the membership rank of 0.076 was at a high
Developing a Fuzzy Expert System to Predict the Risk of Neonatal Death.
Safdari, Reza; Kadivar, Maliheh; Langarizadeh, Mostafa; Nejad, Ahmadreaza Farzaneh; Kermani, Farzaneh
2016-02-01
This study aims at developing a fuzzy expert system to predict the possibility of neonatal death. A questionnaire was given to Iranian neonatologists and the more important factors were identified based on their answers. Then, a computing model was designed considering the fuzziness of variables having the highest neonatal mortality risk. The inference engine used was Mamdani's method and the output was the risk of neonatal death given as a percentage. To validate the designed system, neonates' medical records real data at a Tehran hospital were used. MATLAB software was applied to build the model, and user interface was developed by C# programming in Visual Studio platform as bilingual (English and Farsi user interface). According to the results, the accuracy, sensitivity, and specificity of the model were 90%, 83% and 97%, respectively. The designed fuzzy expert system for neonatal death prediction showed good accuracy as well as proper specificity, and could be utilized in general hospitals as a clinical decision support tool.
Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause's membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples,the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.
The use of fuzzy control system methods for characterizing expert judgment uncertainty distributions
Energy Technology Data Exchange (ETDEWEB)
Smith, R.E.; Booker, J.M.; Bement, T.R.; Parkinson, W.J.; Meyer, M.A. [Los Alamos National Lab., NM (United States); Jamshidi, M. [Univ. of New Mexico, Albuquerque, NM (United States)
1998-12-01
Fuzzy logic methods permit experts to assess parameters affecting performance of components/systems in natural language terms more familiar to them (e.g., high, good, etc.). Recognizing that there is a cost associated with obtaining more precise information, the authors particular interest is in cases where the relationship between the condition of the system and its performance is not well understood, especially for some sets of possible operating conditions, and where developing a better understanding is very difficult and/or expensive. The methods allow the experts to make use of the level of precision with which they understand the underlying process. The authors consider and compare various methods of formulating the process just described, with an application in reliability analysis where expert information forms a significant (if not sole) source of data for reliability analysis. The flow of information through the fuzzy-control-systems based analysis is studied using a simple, hypothetical problem which mimics the structure used to elicit expert information in Parse. They also characterize the effect of using progressively more refined information and examine the use of fuzzy-based methods as data pooling/fusion mechanisms.
A fuzzy expert system to Trust-Based Access Control in crowdsourcing environments
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Olusegun Folorunso
2015-07-01
Full Text Available Crowdsourcing has been widely accepted across a broad range of application areas. In crowdsourcing environments, the possibility of performing human computation is characterized with risks due to the openness of their web-based platforms where each crowd worker joins and participates in the process at any time, causing serious effect on the quality of its computation. In this paper, a combination of Trust-Based Access Control (TBAC strategy and fuzzy-expert systems was used to enhance the quality of human computation in crowdsourcing environment. A TBAC-fuzzy algorithm was developed and implemented using MATLAB 7.6.0 to compute trust value (Tvalue, priority value as evaluated by fuzzy inference system (FIS and finally generate access decision to each crowd-worker. In conclusion, the use of TBAC is feasible in improving quality of human computation in crowdsourcing environments.
Fuzzy Expert System For The Selection Of Tourist Hotels
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GOPAL SINGH
2015-08-01
Full Text Available In the present work a simple and very effective mathematical model is designed for tourist hotels of LEVEL 2. Location of hotels building structure of hotels quality of hotels feedback of hotels and advertisement of hotels are as input factors. Trapezoidal membership function and triangular membership function are used for fuzzification process and defuzzification is done by COG technique. The fuzzy logic has been utilized in several different approaches to modeling the selection of tourist hotels process. This model addressed the hotel of LEVEL2 and this model concludes that the hotel is LEVEL 2 with degree of precision 52.15 .
Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification.
GaneshKumar, Pugalendhi; Rani, Chellasamy; Devaraj, Durairaj; Victoire, T Aruldoss Albert
2014-01-01
Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.
Application of Fuzzy Sets in an Expert System For Technological Process Management
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Josef Tošenovský
2011-12-01
Full Text Available The paper is preoccupied with application of an expert system in the management of a process with one input and one output, using the fuzzy set theory. It resolves the problem of formalization of a verbal description of the process management coupled with the use of process operator’s experience. The procedure that calculates regulatory intervention in the process is presented and accompanied by graphical illustrations.
An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems
Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim
2017-03-01
Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.
Expert system for fault diagnosis in process control valves using fuzzy-logic
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Carneiro, Alvaro L.G., E-mail: carneiro@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Porto Junior, Almir C.S., E-mail: almir@ctmsp.mar.mil.br [Centro Tecnologico da Marinha em Sao Paulo (CIANA/CTMSP), Ipero, SP (Brazil). Centro de Instrucao e Adestramento Nuclear de ARAMAR
2013-07-01
The models of asset maintenance of a process plant basically are classified in corrective maintenance, preventive, predictive and proactive (online). The corrective maintenance is the elementary and most obvious way of the maintenance models. The preventive maintenance consists in a fault prevention work, based on statistical studies that can lead to low efficiency or even an unexpected shutdown of the plant. Predictive maintenance aims to prevent equipment or systems failures through monitoring and tracking of parameters, allowing continuous operation as long as possible. The proactive maintenance usually includes predictive maintenance, emphasizing the root cause analysis of the failure. The maintenance predictive/proactive planning frequently uses software that integrates data from different systems, which facilitates a quick and effective decision- making. In nuclear plants this model has an important role regarding the reliability of equipment and systems. The main focus of this work is to study the development of a model of non-intrusive monitoring and diagnosis applied to process control valves using artificial intelligence by fuzzy logic technique, contributing in the development of predictive methodologies identifying faults in incipient state. The control valve analyzed belongs to a steam plant which simulates the secondary circuit of a PWR nuclear reactor - Pressurized Water Reactor. This study makes use of MATLAB language through the fuzzy logic toolbox which uses the method of inference Mamdani, acting by fuzzy conjunction, through Triangular Norms (t-norm) and Triangular Conorms (t-conorm). As input variables are used air pressure and displacement of the valve stem. Input data coming into the fuzzy system by graph of the automation system Delta V ® available in the plant, which receives a signal of electric current from an 'intelligent' positioned installed on the valve. The output variable is the 'status' of the valve. Through a
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Kim, K.H. [Kangwon National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Hwang, K.J. [Univ. of Ulsan (Korea, Republic of). Dept. of Electrical Engineering; Kim, S.H. [Korea Electric Power Co., Seoul (Korea, Republic of). Power System Control Dept.
1995-08-01
In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.
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Mr.D. V. Kodavade
2014-09-01
Full Text Available With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.
Development of an evolutionary fuzzy expert system for estimating future behavior of stock price
Mehmanpazir, Farhad; Asadi, Shahrokh
2017-07-01
The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a "data mining-based evolutionary fuzzy expert system" (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for
Development of an evolutionary fuzzy expert system for estimating future behavior of stock price
Mehmanpazir, Farhad; Asadi, Shahrokh
2016-07-01
The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a "data mining-based evolutionary fuzzy expert system" (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for
A novel Fuzzy Expert System for the identification of severity of carpal tunnel syndrome.
Kunhimangalam, Reeda; Ovallath, Sujith; Joseph, Paul K
2013-01-01
The diagnosis of carpal tunnel syndrome, a peripheral nerve disorder, at the earliest possible stage is very crucial because if left untreated it may cause permanent nerve damage reducing the chances of successful treatment. Here a novel Fuzzy Expert System designed using MATLAB is proposed for identification of severity of CTS. The data used were the nerve conduction study data obtained from Kannur Medical College, India. It consists of thirteen input fields, which include the clinical values of the diagnostic test and the clinical symptoms, and the output field gives the disease severity. The results obtained match with the expert's opinion with 98.4% accuracy and high degrees of sensitivity and specificity. Since quantification of the intensity of CTS is a crucial step in the electrodiagnostic procedure and is important for defining prognosis and therapeutic measures, such an expert system can be of immense use in those regions where the service of such specialists may not be readily available. It may also prove useful in combination with other systems in providing diagnostic and predictive medical opinions and can add value if introduced into the routine clinical consultations to arrive at the most accurate medical diagnosis in a timely manner.
Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system
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Manikandan Pandiyan
2014-09-01
Full Text Available The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI. Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.
THE DEVELOPMENT OF EXPERT MOOD IDENTIFIER SYSTEM USING FUZZY LOGIC ON BLACKBERRY PLATFORM
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Octavia George
2013-01-01
Full Text Available In our daily life, deciding what caused the bad mood is not easy. This study will design an Expert Mood Identifier System for mobile application. We propose a model that uses 6 variables as the inputs, they are intensity of Sleep (SL, intensity of Eat (ET, hours of using Phone (PH, Spare Time (ST, intensity of Sensitive (SN, intensity of Confidence (CF. These inputs, using Sugeno fuzzy logic, are then fuzzificated to linguistic variables, so that they able to evaluated with the if-then rules. Result of the evaluation will show the highest possibility causes either in Love (LV or density schedule. It will be defuzzificated to a crisp number showing the percentage of what causes it. The experiment results are presented and show the Mood Identifier system is running well on BlackBerry platform and can be used successfully to identify the causes of bad mood with a solution for each case.
A Simple and Effective Remedial Learning System with a Fuzzy Expert System
Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.
2016-01-01
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
A Simple and Effective Remedial Learning System with a Fuzzy Expert System
Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.
2016-01-01
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
Expert System for Competences Evaluation 360° Feedback Using Fuzzy Logic
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Alberto Alfonso Aguilar Lasserre
2014-01-01
Full Text Available Performance evaluation (PE is a process that estimates the employee overall performance during a given period, and it is a common function carried out inside modern companies. PE is important because it is an instrument that encourages employees, organizational areas, and the whole company to have an appropriate behavior and continuous improvement. In addition, PE is useful in decision making about personnel allocation, productivity bonuses, incentives, promotions, disciplinary measures, and dismissals. There are many performance evaluation methods; however, none is universal and common to all companies. This paper proposes an expert performance evaluation system based on a fuzzy logic model, with competences 360° feedback oriented to human behavior. This model uses linguistic labels and adjustable numerical values to represent ambiguous concepts, such as imprecision and subjectivity. The model was validated in the administrative department of a real Mexican manufacturing company, where final results and conclusions show the fuzzy logic method advantages in comparison with traditional 360° performance evaluation methodologies.
Expert system training and control based on the fuzzy relation matrix
Ren, Jie; Sheridan, T. B.
1991-01-01
Fuzzy knowledge, that for which the terms of reference are not crisp but overlapped, seems to characterize human expertise. This can be shown from the fact that an experienced human operator can control some complex plants better than a computer can. Proposed here is fuzzy theory to build a fuzzy expert relation matrix (FERM) from given rules or/and examples, either in linguistic terms or in numerical values to mimic human processes of perception and decision making. The knowledge base is codified in terms of many implicit fuzzy rules. Fuzzy knowledge thus codified may also be compared with explicit rules specified by a human expert. It can also provide a basis for modeling the human operator and allow comparison of what a human operator says to what he does in practice. Two experiments were performed. In the first, control of liquid in a tank, demonstrates how the FERM knowledge base is elicited and trained. The other shows how to use a FERM, build up from linguistic rules, and to control an inverted pendulum without a dynamic model.
Selected Aircraft Throttle Controller With Support Of Fuzzy Expert Inference System
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Żurek Józef
2014-12-01
Full Text Available The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference. The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.
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Pimonov Alexander
2017-01-01
Full Text Available This paper presents the multipurpose approach to evaluation of research and innovation projects based on the method of analysis of hierarchies and fuzzy logics for the mining industry. The approach, implemented as part of a decision support system, can reduce the degree of subjectivity during examinations by taking into account both quantitative and qualitative characteristics of the compared innovative alternatives; it does not depend on specific conditions of examination and allows engagement of experts of various fields of knowledge. The system includes the mechanism of coordination of several experts’ views. Using of fuzzy logics allows evaluating the qualitative characteristics of innovations in the form of formalized logical conclusions.
Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O
2012-06-01
It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain.
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Chong Wu
2014-01-01
Full Text Available As an important content in fuzzy mathematics, similarity measure is used to measure the similarity degree between two fuzzy sets. Considering the existing similarity measures, most of them do not consider the hesitancy degree and some methods considering the hesitancy degree are based on the intuitionistic fuzzy sets, intuitionistic fuzzy values. It may cause some counterintuitive results in some cases. In order to make up for the drawback, we present a new approach to construct the similarity measure between two interval-valued intuitionistic fuzzy sets using the entropy measure and considering the hesitancy degree. In particular, the proposed measure was demonstrated to yield a similarity measure. Besides, some examples are given to prove the practicality and effectiveness of the new measure. We also apply the similarity measure to expert system to solve the problems on pattern recognition and the multicriteria group decision making. In these examples, we also compare it with existing methods such as other similarity measures and the ideal point method.
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Ashima Aggarwal
2014-07-01
Full Text Available Performance Appraisal of employees plays a very critical role towards the growth of any organization. It has always been a tough task for any industry or organization as there is no unanimous scientific modus operandi for that. Performance Appraisal system is used to assess the capabilities and productiveness of the employees. In assessing employee performance, performance appraisal commonly includes assigning numerical values or linguistic labels to employees performance. However, the employee performance appraisal may include judgments which are based on imprecise data particularly when one employee tries to interpret another employee’s performance. Thus, the values assigned by the appraiser are only approximations and there is inherent vagueness in the evaluation. By fuzzy logic perspective, the performance of the appraisee includes the evaluation of his/her work ability, skills and adaptability which are absolutely fuzzy concepts that needs to be define in fuzzy terms. Hence, fuzzy approach can be used to examine these imprecise and uncertainty information. Consequently, the performance appraisal of employees can be accomplished by fuzzy logic approach and different defuzzification techniques are applied to rank the employees according to their performance, which shows inconsequential deviation in the rankings and hence proves the robustness of the system.
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Yaroslav MATSYSHYN
2014-03-01
Full Text Available Sawing solid wood (lumber, wooden boards into blanks is an important technological operation, which has significant influence on the efficiency of the woodworking industry as a whole. Selecting a rational variant of lumber cutting is a complex multicriteria problem with many stochastic factors, characterized by incomplete information and fuzzy attributes. About this property by currently used automatic optimizing cross-cut saw is not always rational use of wood raw material. And since the optimization algorithms of these saw functions as a “black box”, their improvement is not possible. Therefore topical the task of developing a new approach to the optimal cross-cutting that takes into account stochastic properties of wood as a material from biological origin. Here we propose a new approach to the problem of lumber optimal cutting in the conditions of uncertainty of lumber quantity and fuzziness lengths of defect-free areas. To account for these conditions, we applied the methods of fuzzy sets theory and used a genetic algorithm to simulate the process of human learning in the implementation the technological operation. Thus, the rules of behavior with yet another defect-free area is defined in fuzzy expert system that can be configured to perform specific production tasks using genetic algorithm. The author's implementation of the genetic algorithm is used to set up the parameters of fuzzy expert system. Working capacity of the developed system verified on simulated and real-world data. Implementation of this approach will make it suitable for the control of automated or fully automatic optimizing cross cutting of solid wood.
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Li L. H.
2006-11-01
Full Text Available We have developed a fuzzy expert system (XPROS based on the fuzzy multicriteria decision-making paradigm for prospect evaluation. XPROS considers all available information, which are usually fragmentary and imprecise - the kind of information which cannot be incorporated in traditional methods. In essence, XPROS employs fuzzy implications to gauge satisfaction levels associated with each measure of the characteristics associated with the source rock, reservoir, trap, and seal. In this paper we describe XPROS illustrated with a case study. Nous avons développé un système expert flou, XPROS, qui s'appuie sur le paradigme de la prise de décision multicritères en univers incertain pour évaluer un prospect. XPROS utilise l'ensemble des informations disponibles, qu'on ne peut en général prendre en compte dans les méthodes conventionnelles, car elles sont souvent partielles et imprécises. XPROS exploite des implications floues pour calculer les niveaux de satisfaction associés aux diverses mesures des caractéristiques du prospect : roche-mère, réservoir, piège, couverture. Cet article contient la description de XPROS et une illustration sur un cas d'application.
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
2016-04-01
This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to
Arregui, María C; Sánchez, Daniel; Althaus, Rafael; Scotta, Roberto R; Bertolaccini, Isabel
2010-07-01
The introduction of transgenic soybean (Glycine max, L.) varieties resistant to glyphosate (GR soybeans) has rapidly expanded in Argentina, increasing pesticide use where only grasslands were previously cultivated. The authors compared an estimate of environmental risk for different crops and active ingredients using the IPEST index, which is based on a fuzzy-logic expert system. For IPEST calculations, four modules are defined, one reflecting the rate of application, the other three reflecting the risk for groundwater, surface water and air. The input variables are pesticide properties, site-specific conditions and characteristics of the pesticide application. The expert system calculates the value of modules according to the degree of membership of the input variables to the fuzzy subsets F (favourable) and U (unfavourable), and they can be aggregated following sets of decision rules. IPEST integrated values of >or= 7 reflect low environmental risk, and values of wheat showed IPEST values over 7 (low risk), while maize had the lowest IPEST values (high risk). Comparing active ingredients applied in annual and perennial crops, atrazine and acetochlor gave the highest risks of environmental contamination, and they are mainly used in maize. Groundwater was the most affected compartment. Fuzzy logic provided an easy tool combining different environmental components with pesticide properties to give a simple and accessible risk assessment. These findings provide information about active ingredients that should be replaced in order to protect water and air from pesticide contamination. Copyright (c) 2010 Society of Chemical Industry.
Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.
Trnka, Hjalte; Wu, Jian X; Van De Weert, Marco; Grohganz, Holger; Rantanen, Jukka
2013-12-01
Freeze-drying of peptide and protein-based pharmaceuticals is an increasingly important field of research. The diverse nature of these compounds, limited understanding of excipient functionality, and difficult-to-analyze quality attributes together with the increasing importance of the biosimilarity concept complicate the development phase of safe and cost-effective drug products. To streamline the development phase and to make high-throughput formulation screening possible, efficient solutions for analyzing critical quality attributes such as cake quality with minimal material consumption are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.
A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis
Directory of Open Access Journals (Sweden)
Mostafa Langarizadeh
2015-05-01
Full Text Available Introduction Bacterial meningitis is a known infectious disease which occurs at early ages and should be promptly diagnosed and treated. Bacterial and aseptic meningitis are hard to be distinguished. Therefore, physicians should be highly informed and experienced in this area. The main aim of this study was to suggest a system for distinguishing between bacterial and aseptic meningitis, using fuzzy logic. Materials and Methods In the first step, proper attributes were selected using Weka 3.6.7 software. Six attributes were selected using Attribute Evaluator, InfoGainAttributeEval, and Ranker search method items. Then, a fuzzy inference engine was designed using MATLAB software, based on Mamdani’s fuzzy logic method with max-min composition, prod-probor, and centroid defuzzification. The rule base consisted of eight rules, based on the experience of three specialists and information extracted from textbooks. Results Data were extracted from 106 records of patients with meningitis (42 cases with bacterial meningitis in order to evaluate the proposed system. The system accuracy, specificity, and sensitivity were 89%, 92 %, and 97%, respectively. The area under the ROC curve was 0.93, and Kappa test revealed a good level of agreement (k=0.84, P
Qi, Na; Zhang, Zhuoyong; Xiang, Yuhong; Yang, Yuping; Harrington, Peter de B
2015-01-01
Combined with terahertz time-domain spectroscopy, the feasibility of fast and reliable diagnosis of cervical carcinoma by a fuzzy rule-building expert system (FuRES) and a fuzzy optimal associative memory (FOAM) had been studied. The terahertz spectra of 52 specimens of cervix were collected in the work. The original data of samples were preprocessed by Savitzky-Golay first derivative (χderivative), principal component orthogonal signal correction (PC-OSC) and emphatic orthogonal signal correction to improve the performance of FuRES and FOAM models. The effect of the different pretreating methods to improve prediction accuracy was evaluated. The FuRES and FOAM models were validated using bootstrapped Latin-partition method. The obtained results showed that the FuRES and FOAM model optimized with the combination S-G first derivative and PC-OSC method had the better predictive ability with classification rates of 92.9 ± 0.4 and 92.5 ± 0.4 %, respectively. The proposed procedure proved that terahertz spectroscopy combined with fuzzy classifiers could supply a technology which has potential for diagnosis of cancerous tissue.
A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.
Zarinbal, M; Fazel Zarandi, M H; Turksen, I B; Izadi, M
2015-10-01
The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.
Pulido-Velazquez, Manuel; Macian-Sorribes, Hector; María Benlliure-Moreno, Jose; Fullana-Montoro, Juan
2015-04-01
Water resources systems in areas with a strong tradition in water use are complex to manage by the high amount of constraints that overlap in time and space, creating a complicated framework in which past, present and future collide between them. In addition, it is usual to find "hidden constraints" in system operations, which condition operation decisions being unnoticed by anyone but the river managers and users. Being aware of those hidden constraints requires usually years of experience and a degree of involvement in that system's management operations normally beyond the possibilities of technicians. However, their impact in the management decisions is strongly imprinted in the historical data records available. The purpose of this contribution is to present a methodology capable of assessing operating rules in complex water resources systems combining historical records and expert criteria. Both sources are coupled using fuzzy logic. The procedure stages are: 1) organize expert-technicians preliminary meetings to let the first explain how they manage the system; 2) set up a fuzzy rule-based system (FRB) structure according to the way the system is managed; 3) use the historical records available to estimate the inputs' fuzzy numbers, to assign preliminary output values to the FRB rules and to train and validate these rules; 4) organize expert-technician meetings to discuss the rule structure and the input's quantification, returning if required to the second stage; 5) once the FRB structure is accepted, its output values must be refined and completed with the aid of the experts by using meetings, workshops or surveys; 6) combine the FRB with a Decision Support System (DSS) to simulate the effect of those management decisions; 7) compare its results with the ones offered by the historical records and/or simulation or optimization models; and 8) discuss with the stakeholders the model performance returning, if it's required, to the fifth or the second stage
DEFF Research Database (Denmark)
Hildebrandt, Thomas Troels; Cattani, Gian Luca
2016-01-01
An expert system is a computer system for inferring knowledge from a knowledge base, typically by using a set of inference rules. When the concept of expert systems was introduced at Stanford University in the early 1970s, the knowledge base was an unstructured set of facts. Today the knowledge...... base of expert systems is often given in terms of an ontology, extracted and built from various data sources by employing natural language-processing and statistics. To emphasize such capabilities, the term “expert” is now often replaced by “cognitive,” “knowledge,” “knowledge-based,” or “intelligent......” system. With very few exceptions, general-purpose expert systems have failed to emerge so far. However, expert systems are applied in specialized domains, particularly in healthcare. The increasing availability of large quantities of data to organizations today provides a valuable opportunity...
Semeraro, Teodoro; Mastroleo, Giovanni; Aretano, Roberta; Facchinetti, Gisella; Zurlini, Giovanni; Petrosillo, Irene
2016-03-01
A significant threat to the natural and cultural heritage of Mediterranean natural protected areas (NPAs) is related to uncontrolled fires that can cause potential damages related to the loss or a reduction of ecosystems. The assessment and mapping of the vulnerability to fire can be useful to reduce landscape damages and to establish priority areas where it is necessary to plan measures to reduce the fire vulnerability. To this aim, a methodology based on an interactive computer-based system has been proposed in order to support NPA's management authority for the identification of vulnerable hotspots to fire through the selection of suitable indicators that allow discriminating different levels of sensitivity (e.g. Habitat relevance, Fragmentation, Fire behavior, Ecosystem Services, Vegetation recovery after fire) and stresses (agriculture, tourism, urbanization). In particular, a multi-criteria analysis based on Fuzzy Expert System (FES) integrated in a GIS environment has been developed in order to identify and map potential "hotspots" of fire vulnerability, where fire protection measures can be undertaken in advance. In order to test the effectiveness of this approach, this approach has been applied to the NPA of Torre Guaceto (Apulia Region, southern Italy). The most fire vulnerable areas are the patch of century-old forest characterized by high sensitivity and stress, and the wetlands and century-old olive groves due to their high sensitivity. The GIS fuzzy expert system provides evidence of its potential usefulness for the effective management of natural protected areas and can help conservation managers to plan and intervene in order to mitigate the fire vulnerability in accordance with conservation goals. Copyright © 2015 Elsevier Ltd. All rights reserved.
油层保护模糊专家系统的分析与设计%Analysis and design of reservoir protection fuzzy expert system
Institute of Scientific and Technical Information of China (English)
张丹; 曹谢东; 魏存挡; 庞扬
2012-01-01
保护油层技术是油田勘探开发过程中提高勘探开发效果的重要措施之一.将基于模糊推理技术建立的模糊专家系统原型有机地与油层保护知识相结合,开发的油层保护模糊专家系统,充分利用和集成了人类油层保护专家的经验和研究成果以及实验室数据分析资料,为油层保护工程师提供了各类储层敏感性预测的智能决策支持.%The reservoir protection technology is an important measures of developing the effect of oilfield exploration. Fuzzy expert system prototype based on the theory of fuzzy logic and fuzzy reasoning techniques combines with reservoir protection knowledge, developing of the reservoir protection fuzzy expert system, makes full using and integration experience and research findings of human reservoir protection experts and the laboratory data analysis material. The system provides decision - support measures for various types of reservoir sensitivity prediction.
Jaya, T; Dheeba, J; Singh, N Albert
2015-12-01
Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.
Expert Systems: What Is an Expert System?
Duval, Beverly K.; Main, Linda
1994-01-01
Describes expert systems and discusses their use in libraries. Highlights include parts of an expert system; expert system shells; an example of how to build an expert system; a bibliography of 34 sources of information on expert systems in libraries; and a list of 10 expert system shells used in libraries. (Contains five references.) (LRW)
Expert Systems: What Is an Expert System?
Duval, Beverly K.; Main, Linda
1994-01-01
Describes expert systems and discusses their use in libraries. Highlights include parts of an expert system; expert system shells; an example of how to build an expert system; a bibliography of 34 sources of information on expert systems in libraries; and a list of 10 expert system shells used in libraries. (Contains five references.) (LRW)
Ahmed, Hameed Kaleel; Zulquernain, Mallick
2009-01-01
Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible design parameters in minimum possible time. This study aims to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, exposure time, and age of the operators doing complex type of task.
Design of interpretable fuzzy systems
Cpałka, Krzysztof
2017-01-01
This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis
Directory of Open Access Journals (Sweden)
Mostafa Langarizadeh
2015-05-01
Data were extracted from 106 records of patients with meningitis (42 cases with bacterial meningitis in order to evaluate the proposed system. The system accuracy, specificity, and sensitivity were 89%, 92 %, and 97%, respectively. The area under the ROC curve was 0.93, and Kappa test revealed a good level of agreement (k=0.84, P
Domínguez Hernández, Karem R; Aguilar Lasserre, Alberto A; Posada Gómez, Rubén; Palet Guzmán, José A; González Sánchez, Blanca E
2013-01-01
Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN.
Directory of Open Access Journals (Sweden)
Karem R. Domínguez Hernández
2013-01-01
Full Text Available Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC. The expert system consists of 3 phases: (1 risk diagnosis which consists of the interpretation of a patient’s clinical background and the risks for contracting CN according to specialists; (2 cytology images detection which consists of image interpretation (IM and the Bethesda system for cytology interpretation, and (3 determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN.
A Fuzzy Expert System for Fault Management of Water Supply Recovery in the ALSS Project
Tohala, Vapsi J.
1998-01-01
Modeling with a new software is a challenge. CONFIG is a challenge and is design to work with many types of systems in which discrete and continuous processes occur. The CONFIG software was used to model the two subsystem of the Water Recovery system: ICB and TFB. The model worked manually only for water flows with further implementation to be done in the future. Activities in the models are stiff need to be implemented based on testing of the hardware for phase III. More improvements to CONFIG are in progress to make it a more user friendly software.
Chen, Guanrong
2005-01-01
Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subject, the authors move directly into presenting real-world applications of fuzzy logic, revealing its practical flavor. This practicality is then followed by basic fuzzy systems theory. The book also offers a tutorial on fuzzy control theory, based mainly on th
Norouzi, Jamshid; Yadollahpour, Ali; Mirbagheri, Seyed Ahmad; Mazdeh, Mitra Mahdavi; Hosseini, Seyed Ahmad
2016-01-01
Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.
Sengur, Abdulkadir
2008-03-01
In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
William W. Weiss
2001-09-30
Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized database and computer maps generated by neural networks, is being developed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This FEE Tool can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices, and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. As a result, today's pool of experts is much reduced. The FEE Tool will benefit a diverse group in the U.S., leading to a more efficient use of scarce funds and lower product prices for consumers. This fifth of ten semi-annual reports contains a summary of progress to date, problems encountered, plans for the next year, and an assessment of the prospects for future progress. The emphasis during the May 2001 through September 2001 was directed toward development of rules for the fuzzy system.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
Robert Balch
2003-04-15
Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized database and computer maps generated by neural networks, is being developed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This FEE Tool can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices, and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The pool of experts is much reduced today. The FEE Tool will benefit a diverse group in the U.S., leading to a more efficient use of scarce funds, and possibly decreasing dependence on foreign oil and lower product prices for consumers. This fourth of five annual reports contains a summary of progress to date, problems encountered, plans for the next year, and an assessment of the prospects for future progress. The emphasis during the April 2002 through March 2003 period was directed toward Silurian-Devonian geology, development of rules for the fuzzy system, and on-line software.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
Robert Balch
2003-10-15
Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized database and computer maps generated by neural networks, is being developed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This FEE Tool can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices, and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The FEE Tool will benefit a diverse group in the U.S., leading to a more efficient use of scarce funds, and possibly decreasing dependence on foreign oil and lower product prices for consumers. This ninth of ten semi-annual reports contains a summary of progress to date, problems encountered, plans for the next year, and an assessment of the prospects for future progress. The emphasis during the March 2003 through September 2003 period was directed toward Silurian-Devonian geology, development of rules for the fuzzy system, and on-line software.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
Robert Balch
2004-04-08
Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized database and computer maps generated by neural networks, is being developed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This FEE Tool can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices, and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The FEE Tool will benefit a diverse group in the U.S., leading to a more efficient use of scarce funds, and possibly decreasing dependence on foreign oil and lower product prices for consumers. This fifth annual (and tenth of 12 semi-annual reports) contains a summary of progress to date, problems encountered, plans for the next year, and an assessment of the prospects for future progress. The emphasis during the March 2003 through March 2004 period was directed toward completion of the Brushy Canyon FEE Tool and to Silurian-Devonian geology, and development of rules for the Devonian fuzzy system, and on-line software.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
William W. Weiss
2000-12-31
Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries, including medical diagnostics, have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized data base and computer maps generated by neural networks, is proposed for development through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This tool will be beneficial in many regions of the US, enabling risk reduction in oil and gas prospecting and decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the US as reserves are depleted. The proposed expert exploration tool will benefit a diverse group in the US, leading to a more efficient use of scarce funds and lower product prices for consumers. This third of ten semi-annual reports contains an account of the progress, problems encountered, plans for the next quarter, and an assessment of the prospects for future progress.
Adiga, Sadashiv
1984-01-01
Discusses: (1) the architecture of expert systems; (2) features that distinguish expert systems from conventional programs; (3) conditions necessary to select a particular application for the development of successful expert systems; (4) issues to be resolved when building expert systems; and (5) limitations. Examples of selected expert systems…
Inan, Ozlem; Arslan, Derya; Taşdemir, Sakir; Ozcan, Mehmet Musa
2011-08-01
Effects of heat treatment on physicochemical characteristics and sensory properties of different fruit juice concentrates (pekmez) were studied. Apricot pekmez had the highest viscosity followed by mulberry and date pekmez. Apricot and date pekmez had higher scores for odour, taste and consistency than others. Hydroxymethyl furfural concentration of all pekmez samples increased after heat treatments. Samples heated at 75°C showed highest L* values while at 65°C the lowest mean L* values. L* values of all pekmez samples were similar while carob Pekmez had higher L* values. Also a development of the Fuzzy Expert System (FES) was made for prediction. Using the experimental values, FES model of the system was designed. Accordance was found with experimental and FES results when compared statistically. This study provides advantage for prediction possibility of unknown sub-values', which were not experimentally studied.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
Robert S. Balch; Ron Broadhead
2005-03-01
Incomplete or sparse data such as geologic or formation characteristics introduce a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results when working with sparse data. State-of-the-art expert exploration tools, relying on a database, and computer maps generated by neural networks and user inputs, have been developed through the use of ''fuzzy'' logic, a mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk has been reduced with the use of these properly verified and validated ''Fuzzy Expert Exploration (FEE) Tools.'' Through the course of this project, FEE Tools and supporting software were developed for two producing formations in southeast New Mexico. Tools of this type can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In today's oil industry environment, many smaller exploration companies lack the resources of a pool of expert exploration personnel. Downsizing, volatile oil prices, and scarcity of domestic exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The FEE Tools benefit a diverse group in the U.S., allowing a more efficient use of scarce funds, and potentially reducing dependence on foreign oil and providing lower product prices for consumers.
Directory of Open Access Journals (Sweden)
Nurul Hazrina Idris
2017-06-01
Full Text Available This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES, a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES’ aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of radar altimetry data closer to the coast. The principles of CAWRES are twofold. The first is to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system. The second is to minimise the relative offset in the retrieved sea levels caused by switching from one retracker to another using a neural network. The innovative system is validated against geoid height and tide gauges in the Great Barrier Reef, Australia for Jason-1 and Jason-2 satellite missions. The regional investigations have demonstrated that the CAWRES can effectively enhance the quality of 20 Hz sea level data and recover up to 16% more data than the standard MLE4 retracker over the tested region. Comparison against tide gauge indicates that the CAWRES sea levels are more reliable than those of Sensor Geophysical Data Records (SGDR products, because the former has a higher (≥0.77 temporal correlation and smaller (≤19 cm root mean square errors. The results demonstrate that the CAWRES can be applied to coastal regions elsewhere as well as other satellite altimeter missions.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
William W. Weiss
2000-06-30
Incomplete or sparse information on geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. Expert systems have been developed and used in several disciplines and industries, including medical diagnostics, with favorable results. A state-of-the-art exploration ''expert'' tool, relying on a computerized data base and computer maps generated by neural networks, is proposed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. This project will develop an Artificial Intelligence system that will draw upon a wide variety of information to provide realistic estimates of risk. ''Fuzzy logic,'' a system of integrating large amounts of inexact, incomplete information with modern computational methods to derive usable conclusions, has been demonstrated as a cost-effective computational technology in many industrial applications. During project year 1, 90% of geologic, geophysical, production and price data were assimilated for installation into the database. Logs provided geologic data consisting of formation tops of the Brushy Canyon, Lower Brushy Canyon, and Bone Springs zones of 700 wells used to construct regional cross sections. Regional structure and isopach maps were constructed using kriging to interpolate between the measured points. One of the structure derivative maps (azimuth of curvature) visually correlates with Brushy Canyon fields on the maximum change contours. Derivatives of the regional geophysical data also visually correlate with the location of the fields. The azimuth of maximum dip approximately locates fields on the maximum change contours. In a similar manner the second derivative in the x-direction of the gravity map visually correlates with the alignment of the known fields. The visual correlations strongly suggest that neural network architectures will be
Lamastra, L; Balderacchi, M; Di Guardo, A; Monchiero, M; Trevisan, M
2016-12-01
The wine industry is definitely committed in sustainability: the stakeholders' interest for the topic is constantly growing and a wide number of sustainability programs have been launched in recent years. Most of these programs are focusing on the environmental aspects as environmental sustainability indicators, greenhouse gases emissions and the use of Life Cycle Assessment methodology. Among the environmental indicators the carbon and the water footprint are often used. These indicators, while being useful to assess the sustainability performance of the winegrowing farms, do not take into account important aspects related to the agronomic management of the vineyard. To fill this gap a new indicator called "Vigneto" (Vineyard in Italian language) has been developed. "Vigneto" is a multidimensional indicator to evaluate the sustainability of management options adopted at field scale. It considers the main agronomic aspects, which can have an impact on the environment. These include (i) pest management, (ii) soil management (erosion and compaction), (iii) fertility management (soil organic matter management and fertilizer application), (iv) biodiversity management. Those aspects have been related by fuzzy logics and implemented in web GIS software. The application of the model allows obtaining a general judgment of the agronomic sustainability of the vineyard management: the judgment varies from "A" (excellent) to "E" (completely unsustainable). The produced model was validated and tested by four Italian wine estate. The model output reports that the tested wineries have different management strategies: producers manage vineyards in different ways, depending on the different geographical position. The main differences are related to the soil management and to the presence of natural areas different from vineyard. The developed model can be defined as an environmental decision support system that can be used by wine companies' technicians to define the vineyard
d'Agapeyeff, A.
1986-01-01
Intended for potential business users, this paper describes the main characteristics of expert systems; discusses practical use considerations; presents a taxonomy of the systems; and reviews several expert system development projects in business and industry. (MBR)
Expert systems for electric power system operation. Denryoku keito un[prime]yo expert system
Energy Technology Data Exchange (ETDEWEB)
Kunugi, M.; Shimada, K.; Nagata, J. (Toshiba Corp., Tokyo (Japan))
1992-07-01
This review article describes recent noteworthy technological trends in the expert systems for electric power system operation. These technological trends include the development of domain shell for the purpose of facilitating and insuring system development, the integration of expert systems with a conventional energy management system and a SCADA (supervisory control and data acquisition) system, and the integration of an expert system with fuzzy-logic applications and a neural network. This paper also introduces two recent expert systems for electric power system operation. One is the integrated expert system for emergency operations delivered to the Miyagi Load-Dispatching Office of Tohoku Electric Power Company, which consists of the accident reasoning expert system, the inteligent alarm processing, and the accident restoration procedure expert system. Another one is the voltage reactive power control system delivered to Chubu Electric Power Company. 4 refs., 3 figs., 1 tab.
Wang, Zhengfang; Harrington, Peter de B
2013-11-01
A bootstrapped fuzzy rule-building expert system (FuRES) and a bootstrapped t-statistical weight feature selection method were individually used to select informative features from gas chromatography/mass spectrometry (GC/MS) chemical profiles of basil plants cultivated by organic and conventional farming practices. Feature subsets were selected from two-way GC/MS data objects, total ion chromatograms, and total mass spectra, separately. Four economic classifiers based on the bootstrapped FuRES approach, i.e., fuzzy optimal associative memory (e-FOAM), e-FuRES, partial least-squares-discriminant analysis (e-PLS-DA), and soft independent modeling by class analogy (e-SIMCA), and four economic classifiers based on the bootstrapped t-weight approach, i.e., e-PLS-DA-t, e-FOAM-t, e-FuRES-t, and e-SIMCA-t, were constructed thereafter to be compared with full-size classifiers obtained from the entire GC/MS data objects (i.e., FOAM, FuRES, PLS-DA, and SIMCA). By using three features selected from two-way data objects, the average classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 95.3 ± 0.5%, 100%, 100%, and 91.8 ± 0.2%, respectively. The established economic classifiers were used to classify a new validation set collected 2.5 months later with no parametric change to experimental procedure. Classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 96.7%, 100%, 100%, and 96.7%, respectively. Characteristic components in basil extracts corresponding to highest-ranked useful features were putatively identified. The feature subset may prove valuable as a rapid approach for organic basil authentication.
Institute of Scientific and Technical Information of China (English)
CAI Heng
2010-01-01
The expert system is a high-level technology.It is a sub-field of artificial intelligence.We demonstrated the character and software evaluation,carrying out an initial study of expert system.A good development expert system was developed.
RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL
Energy Technology Data Exchange (ETDEWEB)
William W. Weiss
2001-05-17
Incomplete or sparse information on types of data such as geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results. A state-of-the-art exploration ''expert'' tool, relying on a computerized database and computer maps generated by neural networks, is being developed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk can be reduced with the use of a properly developed and validated ''Fuzzy Expert Exploration (FEE) Tool.'' This FEE Tool can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In the 1998-1999 oil industry environment, many smaller exploration companies lacked the resources of a pool of expert exploration personnel. Downsizing, low oil prices, and scarcity of exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The FEE Tool will benefit a diverse group in the U.S., leading to a more efficient use of scarce funds and lower product prices for consumers. This second annual report contains a summary of progress to date, problems encountered, plans for the next quarter, and an assessment of the prospects for future progress. During the second year of the project, data acquisition of the Brushy Canyon Formation was completed with the compiling and analyzing of well logs, geophysical data, and production information needed to characterize production potential in the Delaware Basin. A majority of this data now resides in several online databases on our servers and is in proper form to be accessed by external
Tactical Weather Expert System.
The objective of this project was to assess the feasibility of developing an expert system for tactical weather prediction. Using WILLARD, an expert ...indicate that intelligent interpretations of cloud formations can be made. These inferences can then be automatically passed to the expert system for...processing as another piece of information. It is anticipated that this technology will significantly reduce the dependence of the expert system on a
Persuasiveness of expert systems
Dijkstra, JJ; Liebrand, WBG; Timminga, E; Liebrand, Wim B.G.
1998-01-01
Expert system advice is not always evaluated by examining its contents. Users can be persuaded by expert system advice because they have certain beliefs about advice given by a computer. The experiment in this paper shows that subjects (n = 84) thought that, given the same argumentation, expert syst
Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors
Institute of Scientific and Technical Information of China (English)
DONG Mingchui; CHEANG Takson; SEKAR Booma Devi; CHAN Sileong
2008-01-01
Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.
Xu, Zhanfeng; Bunker, Christopher E; Harrington, Peter de B
2010-11-01
Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.
Institute of Scientific and Technical Information of China (English)
彭俏; 郭立峰; 马杰
2012-01-01
With the increase of the nuclear power plant expert system knowledge base, the consistency checking of knowledge base becomes difficult, and the diagnostic reasoning efficiency of the system also decreases. To solve this problem, fuzzy Petri net was used in knowledge representation and reasoning of the expert system. Fuzzy Petri net analysis techniques was used in calibration and maintenance of the knowledge base, and the diagnostic reasoning was carried out based on diagnostic rules and operating mechanism of Petri net. Simulation results show that the fuzzy Petri net is used in expert system, expert system can effectively carry out the knowledge base consistency test and fault diagnostic reasoning.%随着核动力装置专家系统知识库的增大,知识库的一致性检验变得困难,系统的诊断推理效率也随之下降.为了解决这一问题,将模糊Petri网用于专家系统的知识表示和推理.利用模糊Petri网的分析技术,对知识库进行校验和维护,以诊断规则的Petri网模型为基础,遵照Petri网的运行机制进行诊断推理.仿真实验表明,将模糊Petri网应用于专家系统,可有效进行知识库一致性检验和故障诊断推理.
Benchmarking expert system tools
Riley, Gary
1988-01-01
As part of its evaluation of new technologies, the Artificial Intelligence Section of the Mission Planning and Analysis Div. at NASA-Johnson has made timing tests of several expert system building tools. Among the production systems tested were Automated Reasoning Tool, several versions of OPS5, and CLIPS (C Language Integrated Production System), an expert system builder developed by the AI section. Also included in the test were a Zetalisp version of the benchmark along with four versions of the benchmark written in Knowledge Engineering Environment, an object oriented, frame based expert system tool. The benchmarks used for testing are studied.
Multiple Fuzzy Classification Systems
Scherer, Rafał
2012-01-01
Fuzzy classiﬁers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientiﬁc and business applications. Fuzzy classiﬁers use fuzzy rules and do not require assumptions common to statistical classiﬁcation. Rough set theory is useful when data sets are incomplete. It deﬁnes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classiﬁcation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ﬁnite set of learning models, usually weak learners. The present book discusses the three aforementioned ﬁelds – fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed o...
Expert PC Troubleshooter With Fuzzy-Logic And Self-Learning Support
Bassil, Youssef
2012-01-01
Expert systems use human knowledge often stored as rules within the computer to solve problems that generally would entail human intelligence. Today, with information systems turning out to be more pervasive and with the myriad advances in information technologies, automating computer fault diagnosis is becoming so fundamental that soon every enterprise has to endorse it. This paper proposes an expert system called Expert PC Troubleshooter for diagnosing computer problems. The system is composed of a user interface, a rule-base, an inference engine, and an expert interface. Additionally, the system features a fuzzy-logic module to troubleshoot POST beep errors, and an intelligent agent that assists in the knowledge acquisition process. The proposed system is meant to automate the maintenance, repair, and operations (MRO) process, and free-up human technicians from manually performing routine, laborious, and timeconsuming maintenance tasks. As future work, the proposed system is to be parallelized so as to boo...
Chalmers, Eric; Pedrycz, Witold; Lou, Edmond
2015-03-01
Brace treatment is the most commonly used nonsurgical treatment for adolescents with idiopathic scoliosis. However, brace treatment is not always successful and the factors influencing its success are not completely clear. This makes treatment outcome difficult to predict. A computer model which can accurately predict treatment outcomes could potentially provide valuable treatment recommendations. This paper describes a fuzzy system that includes a prediction model and a decision support engine. The model was constructed using conditional fuzzy c-means clustering to discover patterns in retrospective patient data. The model's ability to predict treatment outcome was compared to the ability of eight Scoliosis experts. The model and experts each predicted treatment outcome retrospectively for 28 braced patients, and these predictions were compared to the actual outcomes. The model outperformed all but one expert individually and performed similarly to the experts as a group. These results suggest that the fuzzy model is capable of providing meaningful treatment recommendations. This study offers the first model for this application whose performance has been shown to be at or above the human expert level.
Syropoulos, Apostolos
2011-01-01
Dialectica categories are a very versatile categorical model of linear logic. These have been used to model many seemingly different things (e.g., Petri nets and Lambek's calculus). In this note, we expand our previous work on fuzzy petri nets to deal with fuzzy topological systems. One basic idea is to use as the dualizing object in the Dialectica categories construction, the unit real interval [0,1], which has all the properties of a {\\em lineale}. The second basic idea is to generalize Vickers's notion of a topological system.
Risk Reduction with a Fuzzy Expert Exploration Tool
Energy Technology Data Exchange (ETDEWEB)
Weiss, William W.; Broadhead, Ron; Sung, Andrew
2000-10-24
This project developed an Artificial Intelligence system that drew up on a wide variety of information in providing realistic estimates of risk. ''Fuzzy logic,'' a system of integrating large amounts of inexact, incomplete information with modern computational methods derived usable conclusions, were demonstrated as a cost-effective computational technology in many industrial applications.
Entropy of Fuzzy Partitions and Entropy of Fuzzy Dynamical Systems
Directory of Open Access Journals (Sweden)
Dagmar Markechová
2016-01-01
Full Text Available In the paper we define three kinds of entropy of a fuzzy dynamical system using different entropies of fuzzy partitions. It is shown that different definitions of the entropy of fuzzy partitions lead to different notions of entropies of fuzzy dynamical systems. The relationships between these entropies are studied and connections with the classical case are mentioned as well. Finally, an analogy of the Kolmogorov–Sinai Theorem on generators is proved for fuzzy dynamical systems.
Directory of Open Access Journals (Sweden)
Panagiotis Petratos
2003-02-01
Full Text Available Heterogeneous research environments, interests and locations do not necessarily coincide, thus hitherto the primary method of communication amongst researchers has been email. In this article a novel unified polythematic, real-time, synergistic, data telecommunication system is proposed with peer-reviewed, bidirectional fuzzy feedback for research scientists, to facilitate scientific information exchange via the extensible markup language (XML on multiple scientific topics, e.g. in mathematics, physics, biology and chemistry.
SENSAT(C) prototype expert system
Lavender, Joseph A.
1994-06-01
The Sensor Satellite Expert System (SENSAT)TM is an application of the concurrent engineering simulation methodology which utilizes fuzzy logic in an object-oriented programming environment. Several unique characteristics of SENSAT includes the implementation team, mission system parameters, and priority optimization with respect to mission, cost, schedule, technology, and funding levels. SENSAT operates within a WINDOWSTM environment and a `simulation tour' is included in this paper along with a video to be shown with an actual SENSAT prototype simulation.
Probability representations of fuzzy systems
Institute of Scientific and Technical Information of China (English)
LI Hongxing
2006-01-01
In this paper, the probability significance of fuzzy systems is revealed. It is pointed out that COG method, a defuzzification technique used commonly in fuzzy systems, is reasonable and is the optimal method in the sense of mean square. Based on different fuzzy implication operators, several typical probability distributions such as Zadeh distribution, Mamdani distribution, Lukasiewicz distribution, etc. are given. Those distributions act as "inner kernels" of fuzzy systems. Furthermore, by some properties of probability distributions of fuzzy systems, it is also demonstrated that CRI method, proposed by Zadeh, for constructing fuzzy systems is basically reasonable and effective. Besides, the special action of uniform probability distributions in fuzzy systems is characterized. Finally, the relationship between CRI method and triple I method is discussed. In the sense of construction of fuzzy systems, when restricting three fuzzy implication operators in triple I method to the same operator, CRI method and triple I method may be related in the following three basic ways: 1) Two methods are equivalent; 2) the latter is a degeneration of the former; 3) the latter is trivial whereas the former is not. When three fuzzy implication operators in triple I method are not restricted to the same operator, CRI method is a special case of triple I method; that is, triple I method is a more comprehensive algorithm. Since triple I method has a good logical foundation and comprises an idea of optimization of reasoning, triple I method will possess a beautiful vista of application.
A Novel Weak Fuzzy Solution for Fuzzy Linear System
Directory of Open Access Journals (Sweden)
Soheil Salahshour
2016-03-01
Full Text Available This article proposes a novel weak fuzzy solution for the fuzzy linear system. As a matter of fact, we define the right-hand side column of the fuzzy linear system as a piecewise fuzzy function to overcome the related shortcoming, which exists in the previous findings. The strong point of this proposal is that the weak fuzzy solution is always a fuzzy number vector. Two complex and non-complex linear systems under uncertainty are tested to validate the effectiveness and correctness of the presented method.
Duda, Richard O.; Shortliffe, Edward H.
1983-01-01
Discusses a class of artificial intelligence computer programs (often called "expert systems" because they address problems normally thought to require human specialists for their solution) intended to serve as consultants for decision making. Also discusses accomplishments (including information systematization in medical diagnosis and…
An Integrated Expert Controller for the Oven Temperature Control System
Directory of Open Access Journals (Sweden)
Nagabhushana KATTE
2011-03-01
Full Text Available Paper presents a methodology for design of integrated fuzzy logic based an expert controller and its implementation for a real time oven temperature control system. Integrated expert controller (IEC is composed by cascading fuzzy logic controller with improved PID controller. Wherein, fuzzy controller evaluates the supplemental control actions and PID evaluates the final control actions. Temperature measurement of the oven with a precision of 16-bits is achieved through Pt100, instrumentation amplifier, and A/D converter and fuzzy plus PID computed control actions are given to the actuator via D/A converter (16-bits and PWM generator. Paper experimentally demonstrated the performance of IEC for oven temperature control application. The performance indexes of the system are presented in a comparative fashion with the conventional PID and expert controllers. Control algorithms are developed using C language.
Duality in Dynamic Fuzzy Systems
Yoshida, Yuji
1995-01-01
This paper shows the resolvent equation, the maximum principle and the co-balayage theorem for a dynamic fuzzy system. We define a dual system for the dynamic fuzzy system, and gives a duality for Snell's optimal stopping problem by the dual system.
Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations
Directory of Open Access Journals (Sweden)
Raheleh Jafari
2017-01-01
Full Text Available The uncertain nonlinear systems can be modeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations. We use the neural networks to approximate the coefficients of the fuzzy equations. The approximation theory for crisp models is extended into the fuzzy equation model. The upper bounds of the modeling errors are estimated. Numerical experiments along with comparisons demonstrate the excellent behavior of the proposed method.
Ionita, C
2014-01-01
The ALICE experiment at CERN employs a number of human operators (shifters), who have to make sure that the experiment is always in a state compatible with taking Physics data. Given the complexity of the system and the myriad of errors that can arise, this is not always a trivial task. The aim of this paper is to describe an expert system that is capable of assisting human shifters in the ALICE control room. The system diagnoses potential issues and attempts to make smart recommendations for troubleshooting. At its core, a Prolog engine infers whether a Physics or a technical run can be started based on the current state of the underlying sub-systems. A separate C++ component queries certain SMI objects and stores their state as facts in a Prolog knowledge base. By mining the data stored in dierent system logs, the expert system can also diagnose errors arising during a run. Currently the system is used by the on-call experts for faster response times, but we expect it to be adopted as a standard tool by reg...
Exploiting expert systems in cardiology: a comparative study.
Economou, George-Peter K; Sourla, Efrosini; Stamatopoulou, Konstantina-Maria; Syrimpeis, Vasileios; Sioutas, Spyros; Tsakalidis, Athanasios; Tzimas, Giannis
2015-01-01
An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients.
Modelling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
LI; Hongxing(李洪兴); WANG; Jiayin(王加银); MIAO; Zhihong(苗志宏)
2002-01-01
A kind of modelling method for fuzzy control systems is first proposed here, which is calledmodelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method thatis different from two well-known modelling methods, that is, the first modelling method, mechanism modellingmethod (MMM), and the second modelling method, system identification modelling method (SlMM). Thismethod can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inferencerules describing a practice system into a kind of nonlinear differential equation with variable coefficients, calledHX equations, so that the mathematical model of the system can be obtained. This means that we solve thedifficult problem of how to get a model represented as differential equations on a complicated or fuzzy controlsystem.
Data Process of Diagnose Expert System based on Neural Network
Directory of Open Access Journals (Sweden)
Shupeng Zhao
2013-12-01
Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network
The mind in the model: capturing expert knowledge with the help of fuzzy logic
Janssen, J.A.E.B.; Schielen, R.M.J.; Augustijn, D.C.M.; Os, van A.G.
2006-01-01
Fuzzy logic offers a way of capturing qualitative knowledge in models. We tested its application in modelling for long term river management planning. We used fuzzy logic to model landscape impacts of different river measures. Preliminary results show that the method allows for modelling expert know
Expert Systems Development Methodology
1989-07-28
expert systems has been hardware development. In the middle 1950’s at the very birth of AI, hardware was large very slow and extremely expensive. In...into another report. For example, MOBPLEX provides output into the Lotus spreadsheet as a semi-automated destination. From the spreadsheet the user of...designed on top of the Lotus 1-2- 3 interface. Lotus was used because it was decided there was no need to build a powerful ad hoc report generator
Expert and Knowledge Based Systems.
Demaid, Adrian; Edwards, Lyndon
1987-01-01
Discusses the nature and current state of knowledge-based systems and expert systems. Describes an expert system from the viewpoints of a computer programmer and an applications expert. Addresses concerns related to materials selection and forecasts future developments in the teaching of materials engineering. (ML)
Expert Systems and Special Education.
Hofmeister, Alan M.; Ferrara, Joseph M.
1986-01-01
The article discusses the characteristics of expert systems (computer programs designed to replicate human expertise in a variety of areas), describes recently available expert system development tools, suggests applications within the field of special education, and reviews recent efforts to apply expert systems technology to special education…
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Fault detection and diagnosis for pneumatic system of automatic production line are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosis instrument are designed. The mathematical model of various pneumatic faults and experimental device are built. In the end, some experiments are done, which shows that the expert system using fuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.
Energy Technology Data Exchange (ETDEWEB)
Ghanei, S., E-mail: Sadegh.Ghanei@yahoo.com [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Vafaeenezhad, H. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Kashefi, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Eivani, A.R. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Mazinani, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of)
2015-04-01
Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency. - Highlights: • New NDT system for microstructural evaluation based on MBN using ANFIS modeling. • Sensitivity of magnetic Barkhausen noise to microstructure changes of the DP steels. • Accurate prediction of martensite by feeding multiple MBN outputs simultaneously. • Obtaining the modeled output without knowing the amount of the used frequency.
Fuzzy fault diagnosis system of MCFC
Institute of Scientific and Technical Information of China (English)
Wang Zhenlei; Qian Feng; Cao Guangyi
2005-01-01
A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.
Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A. R.; Mazinani, M.
2015-04-01
Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency.
Directory of Open Access Journals (Sweden)
Samiul Amin
2009-09-01
Full Text Available Inks, drilling fluids, shower gels and drug delivery vehicles are just a few examples of the many industrial and consumer products based on colloidal and nanostructured complex fluids. The successful formulation of these materials is promoted by understanding how rheological behaviour, which typically dictates performance, relates to underlying microstructure. However, this knowledge can be difficult to obtain for those without the necessary expertise. This article shows how recent developments in rheometer technology address this issue. New rheometers, exemplified by the Kinexus from Malvern have expert knowledge embedded within the instrument and are able to guide users through measurement and data analysis to relevant information. Such systems facilitate development of the design rules to optimize formulations and generate novel and high performance materials of the future.
13. workshop fuzzy systems. Proceedings; 13. Workshop Fuzzy Systeme. Beitraege
Energy Technology Data Exchange (ETDEWEB)
Mikut, R.; Reischl, M. (eds.)
2003-11-01
This volume contains the papers presented at the 13th workshop on fuzzy systems of TC 5.2.2 'Fuzzy Control' of the VDI/VDE-Gesellschaft fuer Mess- und Automatisierungstechnik (GMA) and the TG 'Fuzzy Systems and Soft Computing' of the Gesellschaft fuer Informatik (GI), which took place at Dortmund on November 19-21, 2003. New methods and applications of fuzzy logic, artificial neuronal nets and evolutionary algorithms were presented. The focus was on automation, e.g. in chemical engineering, energy engineering, motor car engineering, robotics and medical engineering. Other applications, e.g. data mining for technical and non-technical applications, were gone into as well. [German] Dieser Tagungsband enthaelt die Beitraege des 13. Workshops ''Fuzzy System'' des Fachausschusses 5.22 ''Fuzzy Control'' der VDI/VDE-Gesellschaft fuer Mess- und Automatisierungstechnik (GMA) und der Fachgruppe ''Fuzzy-Systeme und Soft-Computing'' der Gesellschaft fuer Informatik (GI), der vom 19.-21. November 2003 im Haus Bommerholz, Dortmund, stattfindet. Der jaehrliche Workshop unseres Fachausschusses bietet ein Forum zur Diskussion neuer methodischer Ansaetze und industrieller Anwendungen auf dem Gebiet der Fuzzy-Logik und in angrenzenden Gebieten wie Kuenstlichen Neuronalen Netzen und Evolutionaeren Algorithmen. Besondere Schwerpunkte sind automatisierungstechnische Anwendungen, z.B. in der Verfahrenstechnik, Energietechnik, Kfz-Technik, Robotik und Medizintechnik, aber auch Loesungen in anderen Problemgebieten (z.B. Data Mining fuer technische und nichttechnische Anwendungen) sind von Interesse. (orig.)
Neuro-fuzzy system modeling based on automatic fuzzy clustering
Institute of Scientific and Technical Information of China (English)
Yuangang TANG; Fuchun SUN; Zengqi SUN
2005-01-01
A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
A BENCHMARK EXERCISE ON FUZZY SET THEORY BASED EXPERT JUDGEMENT TECHNIQUE
Institute of Scientific and Technical Information of China (English)
阿谢德; 史习智; 徐济鋆
2001-01-01
It is sometimes impossible to make a correct decision in a certain engineering task without the help from professional expert judgments. Even though there are different expert opinions available, however, they should be appropriately aggregated to a useful form for making an acceptable engineering decision. This paper proposed a technique which utilizes the fuzzy set theory in the aggregation of expert judgments. In the technique, two main key concepts are employed: linguistic variables and fuzzy numbers. Linguistic variables first represent the relative importance of evaluation criteria under consideration and the degree of confidence on each expert perceived by the decision maker, and then are replaced by suitable triangular fuzzy numbers for arithmetic manipulation. As a benchmark problem, the pressure increment in the containment of Sequoyah nuclear power plant due to reactor vessel breach was estimated to verify and validate the proposed technique.
Risk Reduction with a Fuzzy Expert Exploration Tool
Energy Technology Data Exchange (ETDEWEB)
Weiss, William W.; Broadhead, Ron
2002-06-27
In the first three years of the Fee Tool Project, an immense amount of data on the Delaware Basin has been accumulated. Data on geology, structure, production, regional information such as gravity as well as local data, such as well logs. This data, organized and cataloged into several online databases, is available for the Expert System and users as needed and as appropriate in analyzing production potential.
Institute of Scientific and Technical Information of China (English)
秦宏宇; 李建新; 高翔; 王欢; 肖建华; 王洪斌
2016-01-01
为解决中国马产业发展过程中马病兽医专家严重缺乏的问题，该文在系统地挖掘马病专家临床经验与诊断思维的基础上，全面改进了传统的专家系统推理机制与知识表示方法，采用对象-属性-值三元组法（object-attribute-value，O-A-V三元组法）对马病知识进行表示，应用置信系数多值逻辑对知识模糊性进行评价，并在系统中集成模糊规则提升理论（fuzzy rule promotion theory），利用该机制不断调整提升置信系数（promote confidence factor，PCF），进而实现规则置信度的动态、实时调整与优化。最终采用Microsoft.Net操作平台，SQL Server 2008数据库管理工具，研制开发了基于B/S结构的马病辅助诊断专家系统。结果应用临床上已经确诊的大量病例对系统的规则置信度进行动态调整，通过13次样本病例测试，系统诊断符合率由原来的56.47%提高并维持在92.28%。结果表明，该文采用的置信系数多值逻辑知识评价方法与模糊规则提升理论可显著提高系统诊断准确率，为马病辅助诊断专家系统的临床应用奠定了基础，同时也为开发其他动物疾病的诊断专家系统提供了新的思路。%Many diagnosis expert systems for equine had been developed in the past, but there are no in-depth studies for the equine disease diagnosis expert system. And also most of the traditional expert systems are used by the conventional inference approach. The domain expert confidence factor (CF) for each rule of diseases is kept unchanged with its original value in conventional inference approach. So most of the traditional expert systems have a static knowledge base with static inference, and the decision power of these systems remains same through the life cycle of the system. In fact, the progress of equine knowledge requires modification of the knowledge base and rule base in the system. In this paper, we suggested a new
Expert system aids reliability
Energy Technology Data Exchange (ETDEWEB)
Johnson, A.T. [Tennessee Gas Pipeline, Houston, TX (United States)
1997-09-01
Quality and Reliability are key requirements in the energy transmission industry. Tennessee Gas Co. a division of El Paso Energy, has applied Gensym`s G2, object-oriented Expert System programming language as a standard tool for maintaining and improving quality and reliability in pipeline operation. Tennessee created a small team of gas controllers and engineers to develop a Proactive Controller`s Assistant (ProCA) that provides recommendations for operating the pipeline more efficiently, reliably and safely. The controller`s pipeline operating knowledge is recreated in G2 in the form of Rules and Procedures in ProCA. Two G2 programmers supporting the Gas Control Room add information to the ProCA knowledge base daily. The result is a dynamic, constantly improving system that not only supports the pipeline controllers in their operations, but also the measurement and communications departments` requests for special studies. The Proactive Controller`s Assistant development focus is in the following areas: Alarm Management; Pipeline Efficiency; Reliability; Fuel Efficiency; and Controller Development.
Expert Systems for the Analytical Laboratory.
de Monchy, Allan R.; And Others
1988-01-01
Discusses two computer problem solving programs: rule-based expert systems and decision analysis expert systems. Explores the application of expert systems to automated chemical analyses. Presents six factors to consider before using expert systems. (MVL)
Expert Systems as Tools for Technical Communicators.
Grider, Daryl A.
1994-01-01
Discusses expertise, what an expert system is, what an expert system shell is, what expert systems can and cannot do, knowledge engineering and technical communicators, and planning and managing expert system projects. (SR)
Engineering monitoring expert system's developer
Lo, Ching F.
1991-01-01
This research project is designed to apply artificial intelligence technology including expert systems, dynamic interface of neural networks, and hypertext to construct an expert system developer. The developer environment is specifically suited to building expert systems which monitor the performance of ground support equipment for propulsion systems and testing facilities. The expert system developer, through the use of a graphics interface and a rule network, will be transparent to the user during rule constructing and data scanning of the knowledge base. The project will result in a software system that allows its user to build specific monitoring type expert systems which monitor various equipments used for propulsion systems or ground testing facilities and accrues system performance information in a dynamic knowledge base.
Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad
2016-05-01
Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert
A neural network architecture for implementation of expert systems for real time monitoring
Ramamoorthy, P. A.
1991-01-01
Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.
Fuzzy Logic Indoor Positioning System
Directory of Open Access Journals (Sweden)
Roberto García Sánz
2008-12-01
Full Text Available The GPS system is not valid for positioning indoors, thus positioning systems are designed using Wi-Fi technology that allows location of a device inside buildings. The use of fuzzy logic is argued by the failure to find positioning systems based on this technology, which seeks toobserve how their use in this field
Function Approximation Using Probabilistic Fuzzy Systems
J.H. van den Berg (Jan); U. Kaymak (Uzay); R.J. Almeida e Santos Nogueira (Rui Jorge)
2011-01-01
textabstractWe consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems in which the probabilistic nature of uncertainty is taken into account.
Expert systems in clinical microbiology.
Winstanley, Trevor; Courvalin, Patrice
2011-07-01
This review aims to discuss expert systems in general and how they may be used in medicine as a whole and clinical microbiology in particular (with the aid of interpretive reading). It considers rule-based systems, pattern-based systems, and data mining and introduces neural nets. A variety of noncommercial systems is described, and the central role played by the EUCAST is stressed. The need for expert rules in the environment of reset EUCAST breakpoints is also questioned. Commercial automated systems with on-board expert systems are considered, with emphasis being placed on the "big three": Vitek 2, BD Phoenix, and MicroScan. By necessity and in places, the review becomes a general review of automated system performances for the detection of specific resistance mechanisms rather than focusing solely on expert systems. Published performance evaluations of each system are drawn together and commented on critically.
Cornell Mixing Zone Expert System
This page provides an overview Cornell Mixing Zone Expert System water quality modeling and decision support system designed for environmental impact assessment of mixing zones resulting from wastewater discharge from point sources
Diagnosis of arthritis through fuzzy inference system.
Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet
2012-06-01
Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.
Towards the Semantic Web Expert System
Verhodubs, O; Grundspeņķis, J
2011-01-01
The paper presents a conception of the Semantic Web Expert System which is the logical continuation of the expert system development. The Semantic Web Expert System emerges as the result of evolution of expert system concept and it means expert system moving toward the Web and using new Semantic Web technologies. The proposed conception of the Semantic Web Expert System promises to have new useful features that distinguish it from other types of expert systems
Expert Systems for auditing management information systems
Directory of Open Access Journals (Sweden)
Gheroghe Popescu
2007-05-01
Full Text Available Expert systems are built with the help of: specialised programming languages or expert system generators (shell. But this structure was reached after tens of years of work and research, because expert systems are nothing but pragmatic capitalisation of the results of research carried out in artificial intelligence and theory of knowledge.
Using Expert System Job Aids: A Primer.
Carr, Clay
1989-01-01
Explains how current commercial expert system technology can be used to create useful job aids. Expert systems are defined, situations in which an expert system job aid will be most effective are described, expert system shells are discussed, and three commercial expert system products are described. (LRW)
Using Expert System Job Aids: A Primer.
Carr, Clay
1989-01-01
Explains how current commercial expert system technology can be used to create useful job aids. Expert systems are defined, situations in which an expert system job aid will be most effective are described, expert system shells are discussed, and three commercial expert system products are described. (LRW)
Kosko, Bart
1991-01-01
Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
Prediction of low back pain with two expert systems.
Sari, Murat; Gulbandilar, Eyyup; Cimbiz, Ali
2012-06-01
Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.
Indian Academy of Sciences (India)
ANUP KUMAR RAJAK; MALAY NIRAJ; SHALENDRA KUMAR
2016-10-01
In genuine industrial case, problems are inescapable and pose enormous challenges to incorporate accurate sustainability factors into supplier selection. In this present study, three different primarily based multicriteria decision making fuzzy models have been compared with their deterministic version so as to resolve fuzzy prioritization problems. The developed model applies AHP, TOPSIS and fuzzy inference system (FIS)using a MATLAB toolbox to effectively analyze the interdependencies between sustainability criteria and select the best sustainable supplier in the fuzzy environment, while capturing all objective criteria. A typical supplier A4 has been awarded the most suitable supplier with 0.386 composite relative weights of AHP, relative closeness to ideal solution 0.7154 and normalized score index 0.219 FIS model using MATLAB toolbox.
Learning fuzzy logic control system
Lung, Leung Kam
1994-01-01
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the
The Knowledge of Expert Opinion in Intuitionistic Fuzzy Linear Programming Problem
Directory of Open Access Journals (Sweden)
A. Nagoorgani
2015-01-01
Full Text Available In real life, information available for certain situations is vague and such uncertainty is unavoidable. One possible solution is to consider the knowledge of experts on the parameters involved as intuitionistic fuzzy data. We examine a linear programming problem in which all the coefficients are intuitionistic in nature. An approach is presented to solve an intuitionistic fuzzy linear programming problem. In this proposed approach, a procedure for allocating limited resources effectively among competing demands is developed. An example is given to highlight the illustrated study.
A GA-fuzzy automatic generation controller for interconnected power system
CSIR Research Space (South Africa)
Boesack, CD
2011-10-01
Full Text Available This paper presents a GA-Fuzzy Automatic Generation Controller for large interconnected power systems. The design of Fuzzy Logic Controllers by means of expert knowledge have typically been the traditional design norm, however, this may not yield...
Intelligent micro blood typing system using a fuzzy algorithm
Kang, Taeyun; Lee, Seung-Jae; Kim, Yonggoo; Lee, Gyoo-Whung; Cho, Dong-Woo
2010-01-01
ABO typing is the first analysis performed on blood when it is tested for transfusion purposes. The automated machines used in hospitals for this purpose are typically very large and the process is complicated. In this paper, we present a new micro blood typing system that is an improved version of our previous system (Kang et al 2004 Trans. ASME, J. Manuf. Sci. Eng. 126 766, Lee et al 2005 Sensors Mater. 17 113). This system, fabricated using microstereolithography, has a passive valve for controlling the flow of blood and antibodies. The intelligent micro blood typing system has two parts: a single-line micro blood typing device and a fuzzy expert system for grading the strength of agglutination. The passive valve in the single-line micro blood typing device makes the blood stop at the entrance of a micro mixer and lets it flow again after the blood encounters antibodies. Blood and antibodies are mixed in the micro mixer and agglutination occurs in the chamber. The fuzzy expert system then determines the degree of agglutination from images of the agglutinated blood. Blood typing experiments using this device were successful, and the fuzzy expert system produces a grading decision comparable to that produced by an expert conducting a manual analysis.
Quality determination of Mozafati dates using Mamdani fuzzy inference system
Directory of Open Access Journals (Sweden)
N. Alavi
2013-06-01
Full Text Available The date fruit, which is produced mostly in the hot arid regions of Southern Asia and North Africa, in large quantities, is marketed all over the world as an important crop. Date grading is an important process for producers and affects the fruit quality evaluation and export market. In this research Mamdani fuzzy inference system (MFIS was applied as a decision making technique to classify the Mozafati dates based on quality. Two date parameters including the length and freshness were measured for 500 date fruits. These dates were graded by both a human expert and MFIS. Grading results obtained from fuzzy system showed 91% general conformity with the experimental results.
Expert system application education project
Gonzelez, Avelino J.; Ragusa, James M.
1988-01-01
Artificial intelligence (AI) technology, and in particular expert systems, has shown potential applicability in many areas of operation at the Kennedy Space Center (KSC). In an era of limited resources, the early identification of good expert system applications, and their segregation from inappropriate ones can result in a more efficient use of available NASA resources. On the other hand, the education of students in a highly technical area such as AI requires an extensive hands-on effort. The nature of expert systems is such that proper sample applications for the educational process are difficult to find. A pilot project between NASA-KSC and the University of Central Florida which was designed to simultaneously address the needs of both institutions at a minimum cost. This project, referred to as Expert Systems Prototype Training Project (ESPTP), provided NASA with relatively inexpensive development of initial prototype versions of certain applications. University students likewise benefit by having expertise on a non-trivial problem accessible to them at no cost. Such expertise is indispensible in a hands-on training approach to developing expert systems.
Expert System for Earthquake Prediction (ESEP3.0)
Institute of Scientific and Technical Information of China (English)
Wang Wei; Wu Gengfeng; Zhang Bofeng; Li Shengle; Zheng Zhaobi; Lu Yuanzhong
2004-01-01
A brand new expert system for earthquake prediction, called ESEP3.0, was successfullydeveloped recently, in which the fuzzy technology and neural network conception wereincorporated and the steering inference mechanism was introduced. In addition to the functionsof symbol inference and explanation of the first generation of the expert system and theknowledge learning of the second generation, ESEP3.0 has stronger human-machineinteraction function. It consists of knowledge edition, machine learning, steering fuzzyinference engine and synchronous explanation subsystems. In this paper, the components andthe general description of the system are introduced.
US Agency for International Development — ELS is a system based out of the E3 Bureau that provides information on E3 staff, including name, contact information, geographic/sector focus, and expertise. While...
Sun, Xiaobo; Zimmermann, Carolyn M; Jackson, Glen P; Bunker, Christopher E; Harrington, Peter B
2011-01-30
A fast method that can be used to classify unknown jet fuel types or detect possible property changes in jet fuel physical properties is of paramount interest to national defense and the airline industries. While fast gas chromatography (GC) has been used with conventional mass spectrometry (MS) to study jet fuels, fast GC was combined with fast scanning MS and used to classify jet fuels into lot numbers or origin for the first time by using fuzzy rule-building expert system (FuRES) classifiers. In the process of building classifiers, the data were pretreated with and without wavelet transformation and evaluated with respect to performance. Principal component transformation was used to compress the two-way data images prior to classification. Jet fuel samples were successfully classified with 99.8 ± 0.5% accuracy for both with and without wavelet compression. Ten bootstrapped Latin partitions were used to validate the generalized prediction accuracy. Optimized partial least squares (o-PLS) regression results were used as positively biased references for comparing the FuRES prediction results. The prediction results for the jet fuel samples obtained with these two methods were compared statistically. The projected difference resolution (PDR) method was also used to evaluate the fast GC and fast MS data. Two batches of aliquots of ten new samples were prepared and run independently 4 days apart to evaluate the robustness of the method. The only change in classification parameters was the use of polynomial retention time alignment to correct for drift that occurred during the 4-day span of the two collections. FuRES achieved perfect classifications for four models of uncompressed three-way data. This fast GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes.
QUEST: Quality of Expert Systems
Perre, M.
1991-01-01
TNO Physics and Electronics laboratory, in collaboration with the University of Limburg and the Research Institute for Knowledge Systems, worked on a technology project named 'QUEST: Quality of Expert Systems' [FEL90]. QUEST was carried out under commision of the Dutch Ministry of Defence. A strong
QUEST: Quality of Expert Systems
Perre, M.
1991-01-01
TNO Physics and Electronics laboratory, in collaboration with the University of Limburg and the Research Institute for Knowledge Systems, worked on a technology project named 'QUEST: Quality of Expert Systems' [FEL90]. QUEST was carried out under commision of the Dutch Ministry of Defence. A strong
Flight mechanics expert systems
Burns, Rowland E.
1991-01-01
A method is established which can be used to solve any problem in equation-driven disciplines. This is accomplished by solving all applicable equations of the given discipline for all variables which occur in each of the equations. The system then provides logic tests to determine if enough information is available to calculate a new variable. By recording the order in which the equations are used, the machine can also supply a derivation of the answer to each problem.
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Fuzzy logic control for camera tracking system
Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant
1992-01-01
A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.
Bayesian system reliability assessment under fuzzy environments
Energy Technology Data Exchange (ETDEWEB)
Wu, H.-C
2004-03-01
The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayes estimation method will be used to create the fuzzy Bayes point estimator of system reliability by invoking the well-known theorem called 'Resolution Identity' in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.
A Study of Expert System for Career Selection: Literature Review
Directory of Open Access Journals (Sweden)
Waghmode M. L
2015-09-01
Full Text Available Expert system uses human knowledge stored inside a computer to solve problems those require human expertise for solving. Knowledge expert system helps to support for making better decision. There is need of career guidance for students at college level. Expert system plays an important role to facilitate decision making, diagnosis of diseases etc. Expert system for career selection can be developed using Fuzzy logic, neural network for guiding students for selecting proper career stream. From the literature review it has found that in Maharashtra comparatively very less research took place on expert system for career selection. Hence there is wide scope in expert system development for career guidance which will assist secondary and higher secondary students in Maharashtra for selecting proper career. Through this paper researcher thrown light on literature review of career selection expert systems. Here for career selection researcher reviewed 43 literatures including 2-conference proceeding, 8-Books, 22- Journals, 1-Report, 5-Thesis, 3-Websites, 2-Encyclopaedia articles and 2-generic articles. Articles referred are from last two decades and majority of them are latest.
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...
An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management
Naranjo, Rodrigo; Meco, Albert; Arroyo Gallardo, Javier; Santos Peñas, Matilde
2015-01-01
In this work we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produce...
Stability of Cascaded Fuzzy Systems and Observers
Lendek, Z.; Babuska, R.; De Schutter, B.
2009-01-01
A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models with linear or affine consequents. It is well known that the stability of these consequent models does not ensure the stability of the overall fuzzy system. Therefore, several stability conditions have bee
Decomposed fuzzy systems and their application in direct adaptive fuzzy control.
Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang
2014-10-01
In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.
Combined heuristic with fuzzy system to transmission system expansion planning
Energy Technology Data Exchange (ETDEWEB)
Silva Sousa, Aldir; Asada, Eduardo N. [University of Sao Paulo, Sao Carlos School of Engineering, Department of Electrical Engineering Av. Trabalhador Sao-carlense, 400, 13566-590 Sao Carlos, SP (Brazil)
2011-01-15
A heuristic algorithm that employs fuzzy logic is proposed to the power system transmission expansion planning problem. The algorithm is based on the divide to conquer strategy, which is controlled by the fuzzy system. The algorithm provides high quality solutions with the use of fuzzy decision making, which is based on nondeterministic criteria to guide the search. The fuzzy system provides a self-adjusting mechanism that eliminates the manual adjustment of parameters to each system being solved. (author)
Energy Technology Data Exchange (ETDEWEB)
Guimaraes, Antonio Cesar Ferreira [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)
2002-04-01
This work consists of the analysis of natural circulation in a thermal hydraulics loop to a system of passive cooling of a nuclear reactor. The loop in reduced scale is similar to a passive heat removal system of a Pressurized Water Reactor. Using some experts of the area and of the system simulator, a set of fuzzy rules are defined to represent the problem and the associated uncertainties. The results are satisfactory if compared for example to experimental ones. With this model, inferences can be accomplished by the engineer, for adjustment and control of the problem variables. (author)
Cataloging Expert Systems: Optimism and Frustrated Reality.
Olmstadt, William J.
2000-01-01
Discusses artificial intelligence and attempts to catalog expert systems. Topics include the nature of expertise; examples of cataloging expert systems; barriers to implementation; and problems, including total automation, cataloging expertise, priorities, and system design. (LRW)
Minimal solution of singular LR fuzzy linear systems.
Nikuie, M; Ahmad, M Z
2014-01-01
In this paper, the singular LR fuzzy linear system is introduced. Such systems are divided into two parts: singular consistent LR fuzzy linear systems and singular inconsistent LR fuzzy linear systems. The capability of the generalized inverses such as Drazin inverse, pseudoinverse, and {1}-inverse in finding minimal solution of singular consistent LR fuzzy linear systems is investigated.
Expert system application for prioritizing preventive actions for shift work: shift expert.
Esen, Hatice; Hatipoğlu, Tuğçen; Cihan, Ahmet; Fiğlali, Nilgün
2017-09-19
Shift patterns, work hours, work arrangements and worker motivations have increasingly become key factors for job performance. The main objective of this article is to design an expert system that identifies the negative effects of shift work and prioritizes mitigation efforts according to their importance in preventing these negative effects. The proposed expert system will be referred to as the shift expert. A thorough literature review is conducted to determine the effects of shift work on workers. Our work indicates that shift work is linked to demographic variables, sleepiness and fatigue, health and well-being, and social and domestic conditions. These parameters constitute the sections of a questionnaire designed to focus on 26 important issues related to shift work. The shift expert is then constructed to provide prevention advice at the individual and organizational levels, and it prioritizes this advice using a fuzzy analytic hierarchy process model, which considers comparison matrices provided by users during the prioritization process. An empirical study of 61 workers working on three rotating shifts is performed. After administering the questionnaires, the collected data are analyzed statistically, and then the shift expert produces individual and organizational recommendations for these workers.
Expert Systems and Intelligent Information Retrieval.
Brooks, H. M.
1987-01-01
Explores what an intelligent information retrieval system involves and why expert system techniques might interest system designers. Expert systems research is reviewed with emphasis on components, architecture, and computer interaction, and it is concluded that information retrieval is not an ideal problem domain for expert system application at…
Fuzzy stability and synchronization of hyperchaos systems
Energy Technology Data Exchange (ETDEWEB)
Wang Junwei [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China)], E-mail: wangjunweilj@yahoo.com.cn; Xiong Xiaohua [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China); Department of Computer Science, Jiangxi Normal University, Nanchang 330027 (China); Zhao Meichun [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China); Department of Mathematics, Guangdong University of Finance, Gunangzhou 510521 (China); Zhang Yanbin [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China)
2008-03-15
This paper studies stability and synchronization of hyperchaos systems via a fuzzy-model-based control design methodology. First, we utilize a Takagi-Sugeno fuzzy model to represent a hyperchaos system. Second, we design fuzzy-model-based controllers for stability and synchronization of the system, based on so-called 'parallel distributed compensation (PDC)'. Third, we reduce a question of stabilizing and synchronizing hyperchaos systems to linear matrix inequalities (LMI) so that convex programming techniques can solve these LMIs efficiently. Finally, the generalized Lorenz hyperchaos system is employed to illustrate the effectiveness of our designing controller.
PENERAPAN FUZZY INFERENCE SYSTEM TAKAGI-SUGENO-KANG PADA SISTEM PAKAR DIAGNOSA PENYAKIT GIGI
Directory of Open Access Journals (Sweden)
Lutfi Salisa Setiawati
2016-04-01
Full Text Available Generally, expert system only show types of disease after user choose symptoms. In the study is done the addition of disease severity level. The method applied in the calculation of the severity is a method of Fuzzy Inference System Takagi-Sugeno-Kang (Method of Sugeno. This study attempts to know whether method Fuzzy Inference System Takagi-Sugeno-Kang can work for expert system in giving the diagnosis diseases of the teeth. The result of this research or severity for diseases of pulpitis reversible 38,53%, pulpitis irreversible 59,64%, periodontitis 69,62%, acute periodontitis 51,43%, gingivitis 45.5%, acute pericoronitis 53,93%, sub acute pericoronitis 52,14%, chronic pericoronitis 46,05%, caries dentist an early stage 37,61%, caries dentist toward an advanced stage 43,89%, caries dentist an advanced stage 51,76%, gangrene pulpa 42,5%, polyps pulpa 56,43%, and periostitis 58,55%. A conclusion that was obtained from the study that is a method of Fuzzy Inference System Takagi-Sugeno-Kang could be applied to expert system of the teeth. Key Word: Teeth , Expert System , Expert System Teeth , Fuzzy Logic , Fuzzy Inference System , Takagi-Sugeno-Kang , Fuzzy Sugeno Pada umumnya, istem pakar hanya menampilkan jenis penyakit setelah user memilih gejala-gejala. Pada penelitian ini dilakukan penambahan tingkat keparahan penyakit. Metode yang diterapkan dalam perhitungan tingkat keparahan ini yaitu Metode Fuzzy Inference System Takagi-Sugeno-Kang (Metode Sugeno. Penelitian ini bertujuan untuk mengetahui apakah metode Fuzzy Inference System Takagi-Sugeno-Kang dapat diterapkan pada sistem pakar dalam memberikan diagnosa penyakit gigi. Hasil dari penelitian ini didapatkan tingkat keparahan untuk penyakit Pulpitis Reversibel 38,53%, Pulpitis Irreversibel 59,64%, Periodontitis 69,62%, Periodontitis Akut 51,43%, Gingivitis 45,5%, Perikoronitis Akut 53,93%, Perikoronitis Sub Akut 52,14%, Perikoronitis Kronis 46,05%, Karies Denties Tahap Awal 37,61%, Karies
Energy Technology Data Exchange (ETDEWEB)
Souto, Kelling C.; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Lab. de Monitoracao de Processos]. E-mail: kelling@lmp.ufrj.br; schirru@lmp.ufrj.br
2005-07-01
This work develops the start to an expert system, which is able to infer about a generic structure of indicators, in order to monitor, measure and evaluate questions related to project, security operation and human performance according to politics, objectives and goals of the nuclear power plant Angra 2. This structure, organized in graphics, inserted in the context of orientation towards objects, represents the knowledge about the expert system and is mapped out in a fuzzy context. The engine of inference is of backward chaining type associated to a process of search in depths, in such a way that it establishes the representative status of the plant, making possible to analyze and manage the mission of its situation. (author)
Expert System Prototype for False Event Discrimination.
1985-11-14
This report discusses a prototype expert system for event discrimination. We wanted to determine whether applying an expert system to handle and...other potential sources of erroneous information. The expert system is an apt vehicle for growth of systems knowledge, for quick decision making, and
Artificial Hydrocarbon Networks Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Hiram Ponce
2013-01-01
Full Text Available This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.
Adaptive Fuzzy Systems in Computational Intelligence
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
Structural health monitoring using genetic fuzzy systems
Pawar, Prashant M
2014-01-01
The high profile of structural health monitoring (SHM) will add urgency to this detailed treatment of intelligent SHM development and implementation via the evolutionary system, which uses a genetic algorithm to automate the development of the fuzzy system.
Gender Classification by Fuzzy Inference System
2013-01-01
Gender classification from face images has many applications and is thus an important research topic. This paper presents an approach to gender classification based on shape and texture information gathered to design a fuzzy decision making system. Beside face shape features, Zernik moments are applied as system inputs to improve the system output which is considered as the probability of being male face image. After parameters tuning of the proposed fuzzy decision making system, 85.05% class...
Expert System Initiative in Logistic Readiness (EXSYN)
1987-03-01
This initiative is to demonstrate the feasibility of using expert system technology to assist TRADOC combat developers with the assignment of...practices into rule sets; (2) develop a prototype expert system based on the rule sets, using a commercially available expert system development tool
Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning for pitch control system
Li, Yezi; Xiao, Cheng; Sun, Jinhao
2013-03-01
PID and fuzzy PID controller are applied into the pitch control system. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist.
Fuzzy modeling and synchronization of hyper chaotic systems
Energy Technology Data Exchange (ETDEWEB)
Zhang Hongbin [Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)] e-mail: zhanghb@uestc.edu.cn; Liao Xiaofeng [Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China); Institute of Computer Science, Chongqing University, Chongqing 400044 (China); Yu Juebang [Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)
2005-11-01
This paper presents fuzzy model-based designs for synchronization of hyper chaotic systems. The T-S fuzzy models for hyper chaotic systems are exactly derived. Based on the T-S fuzzy hyper chaotic models, the fuzzy controllers for hyper chaotic synchronization are designed via the exact linearization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed method.
Fuzzy Case-Based Reasoning System
Directory of Open Access Journals (Sweden)
Jing Lu
2016-06-01
Full Text Available In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature, according to the maximum degree principle. Thus, we add the “vague” concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.
A New Method for Solving General Dual Fuzzy Linear Systems
Directory of Open Access Journals (Sweden)
M. Otadi
2013-09-01
Full Text Available . According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS cannot be replaced by a fuzzy linear system (FLS. In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derived
Operational expert system applications in Canada
Suen, Ching Y
1992-01-01
This book is part of a new series on operational expert systems worldwide. Expert systems are now widely used in different parts of the world for various applications. The past four years have witnessed a steady growth in the development and deployment of expert systems in Canada. Research in this field has also gained considerable momentum during the past few years. However, the field of expert systems is still young in Canada. This book contains 13 chapters contributed by 31 experts from both universities and industries across Canada covering a wide range of applications related to electric
Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.
Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu
2015-05-01
This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.
Temperature Control System Using Fuzzy Logic Technique
Directory of Open Access Journals (Sweden)
Isizoh A N
2012-06-01
Full Text Available Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. This paper thus presents a fuzzy logic based-temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit. It contains a design approach that uses fuzzy logic technique to achieve a controlled temperature output function.
Language theory and expert systems
Directory of Open Access Journals (Sweden)
Attilio Agodi
1988-11-01
Full Text Available Some remarks on the problem of knowledge representation and processing, as recognized in connection with the use of computers in the scientific research work, emphasizes the relevance of these problems for the studies on both the theory of languages and the expert system. A consideration of the common traits in the recent history of these studies, with reference to the use of computers on texts in natural language motivates the introduction of set theoretic and algebraic methods, suitable for applications in the analysis and in the automatic treatment of languages, based on the concept of model sets and on relational structures suggested from the connections between syntax and semantics evidenced in some example of sub-languages corresponding to theories of different classes of physical phenomena. Some details of these methods are evidenced, which have already successfully used or whose applications appears suggestive of interesting development.
Japanese advances in fuzzy systems research
Schwartz, Daniel G.
1992-07-01
During this past summer (1991), I spent two months on an appointment as visiting researcher at Kansai University, Osaka, Japan, and five weeks at the Laboratory for International Fuzzy Engineering Research (LIFE), in Yokohama. Part of the expenses for the time in Osaka, and all the expenses for the visit at LIFE, were covered by ONR. While there I met with most of the key researchers in both fuzzy systems and case-based reasoning. This involved trips to numerous universities and research laboratories at Matsushita/Panasonic, Omron, and Hitachi Corporations. In addition, I spent three days at the Fuzzy Logic Systems Institute (FLSI), Iizuka, and I attended the annual meeting of the Japan Society for Fuzzy Theory and Research (SOFT-91) in Nagoya. The following report elaborates what I learned as a result of those activities.
Knowledge Processing Method of Fault Diagnosis Expert Systems for Letter Sorting Equipment
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on the analysis of fault diagnosis knowledge of lettersorting machine, this paper proposes a processing method by which the fault diagnosis knowledge is divided into exact knowledge, inadequate knowledge and fuzzy knowledge. Then their presenting and implementing form in fault diagnosis expert system is discussed and studied. It is proved that the expert system has good feasibility in the field of the diagnosis of letter sorting machine.
A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.
Kilic, Yusuf Alper; Kilic, Ilke
2010-12-01
Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors.
A note on the solution of fuzzy transportation problem using fuzzy linear system
Directory of Open Access Journals (Sweden)
P. Senthilkumar
2013-08-01
Full Text Available In this paper, we discuss the solution of a fuzzy transportation problem, with fuzzy quantities. The problem is solved in two stages. In the first stage, the fuzzy transportation problem is reduced to crisp system by using the lower and upper bounds of fuzzy quantities. In the second stage, the crisp transportation problems are solved by usual simplex method. The procedure is illustrated with numerical examples.
Operational expert system applications in Europe
Zarri, Gian Piero
1992-01-01
Operational Expert System Applications in Europe describes the representative case studies of the operational expert systems (ESs) that are used in Europe.This compilation provides examples of operational ES that are realized in 10 different European countries, including countries not usually examined in the standard reviews of the field.This book discusses the decision support system using several artificial intelligence tools; expert systems for fault diagnosis on computerized numerical control (CNC) machines; and expert consultation system for personal portfolio management. The failure prob
Reliability modelling of repairable systems using Petri nets and fuzzy Lambda-Tau methodology
Energy Technology Data Exchange (ETDEWEB)
Knezevic, J.; Odoom, E.R
2001-07-01
A methodology is developed which uses Petri nets instead of the fault tree methodology and solves for reliability indices utilising fuzzy Lambda-Tau method. Fuzzy set theory is used for representing the failure rate and repair time instead of the classical (crisp) set theory because fuzzy numbers allow expert opinions, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into the system model. Petri nets are used because unlike the fault tree methodology, the use of Petri nets allows efficient simultaneous generation of minimal cut and path sets.
Expert systems in treating substance abuse.
Wesson, D R; Hink, R H
1990-01-01
Computer programs can assist humans in solving complex problems that cannot be solved by traditional computational techniques using mathematic formulas. These programs, or "expert systems," are commonly used in finance, engineering, and computer design. Although not routinely used in medicine at present, medical expert systems have been developed to assist physicians in solving many kinds of medical problems that traditionally require consultation from a physician specialist. No expert system...
An Expert System for Asset Reconciliation.
1987-09-01
Expert system technology appears to hold considerable promise for enhancing productivity and promoting better decision-making. The purpose of this...study was to determine if an expert system application for asset reconciliation could improve inventory management procedures and potentially produce...finding that documented a 15 percent increase in the effectiveness of inventory managers when assisted by an expert system . Research was conducted to
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Directory of Open Access Journals (Sweden)
P. Akhavan
2014-10-01
Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Living Expert System (LEXSYS). Volume 6
1989-05-15
PROTOLEX SUBNET DISCUSSIONS: Attack Helicopter Operations; Low Intensity Conflict; Continuous Operations. Keywords: Dialogue; Discussions; Military commanders; Decision making; Decision aids; LEXSYS (Living Expert System ).
Intrusion Detection Approach Using Connectionist Expert System
Institute of Scientific and Technical Information of China (English)
MA Rui; LIU Yu-shu; DU Yan-hui
2005-01-01
In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding neurons, adopts the three-layered feed forward network with full interconnection between layers,translates the feature values into the continuous values belong to the interval [0, 1 ], shows the confidence degree about intrusion detection rules using the weight values of the neural networks and makes uncertain inference with sigmoid function. Compared with the rule-based expert system, the neural network expert system improves the inference efficiency.
A Fuzzy Control Irrigation System For Cottonfield
Zhang, Jun; Zhao, Yandong; Wang, Yiming; Li, Jinping
A fuzzy control irrigation system for cotton field is presented in this paper. The system is composed of host computer, slave computer controller, communication module, soil water sensors, valve controllers, and system software. A fuzzy control model is constructed to control the irrigation time and irrigation quantity for cotton filed. According to the water-required rules of different cotton growing periods, different irrigation strategies can be carried out automatically. This system had been used for precision irrigation of the cotton field in Langfang experimental farm of Soil and Fertilizer Institute, Chinese Academy of Agricultural Sciences in 2006. The results show that the fuzzy control irrigation system can improve cotton yield and save much water quantity than the irrigation system based on simple on-off control algorithm.
Expert Systems: An Overview for Teacher-Librarians.
Orwig, Gary; Barron, Ann
1992-01-01
Provides an overview of expert systems for teacher librarians. Highlights include artificial intelligence and expert systems; the development of the MYCIN medical expert system; rule-based expert systems; the use of expert system shells to develop a specific system; and how to select an appropriate application for an expert system. (11 references)…
Expert Systems: An Overview for Teacher-Librarians.
Orwig, Gary; Barron, Ann
1992-01-01
Provides an overview of expert systems for teacher librarians. Highlights include artificial intelligence and expert systems; the development of the MYCIN medical expert system; rule-based expert systems; the use of expert system shells to develop a specific system; and how to select an appropriate application for an expert system. (11 references)…
Digital Image Enhancement with Fuzzy Interface System
Directory of Open Access Journals (Sweden)
Prabhpreet Kaur
2012-09-01
Full Text Available Present day application requires various version kinds of images and pictures as sources of information for interpretation and analysis. Whenever an image is converted from one form to another, such as, digitizing, scanning, transmitting, storing, etc. Some form of degradation occurs at the output. Hence, the output image has to undergo a process called image enhancement which consist of a collection of techniques that seeks to improve the visual appearances of an image. Image enhancement technique is basically improving the perception of information in images for human viewers and providing 'better' input for other automated image processing techniques. This thesis presents a new approach for image enhancement with fuzzy interface system. Fuzzy techniques can manage the vagueness and ambiguity efficiently (an image can be represented as fuzzy set. Fuzzy logic is a powerful tool to represent and process human knowledge in form of fuzzy if-then rules. Compared to other filtering techniques, fuzzy filter gives the better performance and is able to represent knowledge in a comprehensible way.
A method for solving fully fuzzy linear system with trapezoidal fuzzy numbers
Directory of Open Access Journals (Sweden)
A. Kumar
2010-03-01
Full Text Available Different methods have been proposed for finding the non-negative solution of fully fuzzy linear system (FFLS i.e. fuzzy linear system with fuzzy coefficients involving fuzzy variables. To the best of our knowledge, there is no method in the literature for finding the non-negative solution of a FFLS without any restriction on the coefficient matrix. In this paper a new computational method is proposed to solve FFLS without any restriction on the coefficient matrix by representing all the parameters as trapezoidal fuzzy numbers.
An Expert System for Shipboard Maintenance
1990-09-01
manuals are often difficult to find, maintain, and store, and guides are not easily followed. An expert system for troubleshooting could improve current...integrity of the expert system program. A prototype system for troubleshooting the NAXI 100-2 Low Pressure Air Compressor was developed to illustrate the
Using Expert Systems For Computational Tasks
Duke, Eugene L.; Regenie, Victoria A.; Brazee, Marylouise; Brumbaugh, Randal W.
1990-01-01
Transformation technique enables inefficient expert systems to run in real time. Paper suggests use of knowledge compiler to transform knowledge base and inference mechanism of expert-system computer program into conventional computer program. Main benefit, faster execution and reduced processing demands. In avionic systems, transformation reduces need for special-purpose computers.
Automobile active suspension system with fuzzy control
Institute of Scientific and Technical Information of China (English)
刘少军; 黄中华; 陈毅章
2004-01-01
A quarter-automobile active suspension model was proposed. High speed on/off solenoid valves were used as control valves and fuzzy control was chosen as control method . Based on force analyses of system parts, a mathematical model of the active suspension system was established and simplified by linearization method. Simulation study was conducted with Matlab and three scale coefficients of fuzzy controller (ke, kec, ku) were acquired. And an experimental device was designed and produced. The results indicate that the active suspension system can achieve better vibration isolation performance than passive suspension system, the displacement amplitude of automobile body can be reduced to 55%. Fuzzy control is an effective control method for active suspension system.
An Expert System Approach for Garden Designing
Directory of Open Access Journals (Sweden)
NiloofarMozafari
2012-10-01
Full Text Available In the recent years, the quality of human life is improved by artificial intelligencetechniques. In artificial intelligence, an expert system is a computer system that emulates thedecision-making ability of a human expert. Expert systems are designed to solve complexproblems by reasoning about knowledge, like an expert. In this paper, we propose an expertsystem with the aim of designing the garden with considering the different taste of thepeople. The proposed system can help people to design their garden themselves. Indeed, it isable to use by architectures to provide decision support system, interactive training tool andexpert advice. The system constitutes part of intelligent system of designing the garden. Aninitial evaluation of the expert system was carried out and a positive feedback was receivedfrom the users.
Gender Classification by Fuzzy Inference System
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Payman Moallem
2013-02-01
Full Text Available Gender classification from face images has many applications and is thus an important research topic. This paper presents an approach to gender classification based on shape and texture information gathered to design a fuzzy decision making system. Beside face shape features, Zernik moments are applied as system inputs to improve the system output which is considered as the probability of being male face image. After parameters tuning of the proposed fuzzy decision making system, 85.05% classification rate on the FERET face database (including 1199 individuals from different poses and facial expressions shows acceptable results compare to other methods.
Expert and stakeholder perception: Extracting value from survey data using fuzzy logic
Energy Technology Data Exchange (ETDEWEB)
Waters, D.; Grindrod, P.; Yousaf, F. [QuantiSci Ltd., Thames (United Kingdom)
1996-12-31
The assessment of stakeholder perception and quantitative understanding of respondent data concerning radiological risks is very important for the success of many radioactive waste management programmes. The traditional approach often relies on expert opinions and the use of standard statistical techniques to post process survey results. Such techniques require a number of underlying assumptions (e.g. independence, correlation) to ensure the application of the method is appropriate. The human perception process does not always allow attribute exchangeability, often permitted in statistical approaches. Hence, such snapshots are of limited use for predictive purposes. The most robust procedure is to assess the perceptions and attitudes from a range of respondents including: nuclear safety experts, scientists, public, public in close proximity to potential nuclear sites, and pressure groups. This should be done in a way that identifies whether any underlying logic (inferential rules, linking attributes to response) is exhibited, which could be anticipated or managed in the future. This will ensure all impacting views are incorporated and so lead to a focused information dissemination and communication strategy that will promote positive stakeholder response. The authors propose to analyse the process by which individuals formulate their response to perceived risks. Two sample data sets will be discussed to illustrate how inferential rules can be sought by applying AI techniques based on fuzzy logic. This framework is suited to the subjective, context-dependent, non-unique, classification of attributes and concepts involved in a cognitive approach to perception. Inferences are represented by memberships in hierarchical fuzzy sets. Such a hierarchy is inferred from the relative strengths of different attributes. The objective is to investigate individual evaluation and response to risks within and across different focus groups.
Assessment of the Degree of Consistency of the System of Fuzzy Rules
Directory of Open Access Journals (Sweden)
Pospelova Lyudmila Yakovlevna
2013-12-01
Full Text Available The article analyses recent achievements and publications and shows that difficulties of explaining the nature of fuzziness and equivocation arise in socio-economic models that use the traditional paradigm of classical rationalism (computational, agent and econometric models. The accumulated collective experience of development of optimal models confirms prospectiveness of application of the fuzzy set approach in modelling the society. The article justifies the necessity of study of the nature of inconsistency in fuzzy knowledge bases both on the generalised ontology level and on pragmatic functional level of the logical inference. The article offers the method of search for logical and conceptual contradictions in the form of a combination of the abduction and modus ponens. It discusses the key issue of the proposed method: what properties should have the membership function of the secondary fuzzy set, which describes in fuzzy inference models such a resulting state of the object of management, which combines empirically incompatible properties with high probability. The degree of membership of the object of management in several incompatible classes with respect to the fuzzy output variable is the degree of fuzziness of the “Intersection of all results of the fuzzy inference of the set, applied at some input of rules, is an empty set” statement. The article describes an algorithm of assessment of the degree of consistency. It provides an example of the step-by-step detection of contradictions in statistical fuzzy knowledge bases at the pragmatic functional level of the logical output. The obtained results of testing in the form of sets of incompatible facts, output chains, sets of non-crossing intervals and computed degrees of inconsistency allow experts timely elimination of inadmissible contradictions and, at the same time, increase of quality of recommendations and assessment of fuzzy expert systems.
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Weakly linear systems of fuzzy relation inequalities: The heterogeneous case
Ignjatović, Jelena; Damljanović, Nada; Jančić, Ivana
2011-01-01
New types of systems of fuzzy relation inequalities and equations, called weakly linear, have been recently introduced in [J. Ignjatovi\\'c, M. \\'Ciri\\'c, S. Bogdanovi\\'c, On the greatest solutions to weakly linear systems of fuzzy relation inequalities and equations, Fuzzy Sets and Systems 161 (2010) 3081--3113.]. The mentioned paper dealt with homogeneous weakly linear systems, composed of fuzzy relations on a single set, and a method for computing their greatest solutions has been provided. This method is based on the computing of the greatest post-fixed point, contained in a given fuzzy relation, of an isotone function on the lattice of fuzzy relations. Here we adapt this method for computing the greatest solutions of heterogeneous weakly linear systems, where the unknown fuzzy relation relates two possibly different sets. We also introduce and study quotient fuzzy relational systems and establish relationships between solutions to heterogeneous and homogeneous weakly linear systems. Besides, we point out ...
PV grid connected system with fuzzy intelligent control
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Florin DRAGOMIR
2009-05-01
Full Text Available This paper proposes an engineering solution for stability control of the low voltage electrical networks with distributed power generation from renewable energy resources. First there are presentedgenerally, the existing problems in this type of systems, capable to be solved with automation intelligent control. In the second part, the paper focuses over fuzzy controller design based on experimentalmonitored data and experts know how. The results of proposed software realized with LabView will be pointed out from the main objective point of view.
A fuzzy logic system for seizure onset detection in intracranial EEG.
Rabbi, Ahmed Fazle; Fazel-Rezai, Reza
2012-01-01
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.
12. workshop fuzzy systems. Proceedings; 12. Workshop Fuzzy Systeme. Proceedings
Energy Technology Data Exchange (ETDEWEB)
Mikut, R.; Reischl, M. (eds.)
2002-11-01
This annual workshop is a forum for discussing new methods and industrial applications in fuzzy logic and related fields like artificial neuronal nets and evolutionary algorithms. The focus is on applications in automation, e.g. in chemical engineering, energy engineering, automobile engineering, robotics and medical engineering. Other areas of interest are, e.g. data mining for technical and non-technical applications. [German] Der jaehrliche Workshop unseres Fachausschusses bietet ein Forum zur Diskussion neuer methodischer Ansaetze und industrieller Anwendungen auf dem Gebiet der Fuzzy-Logik und in angrenzenden Gebieten wie Kuenstlichen Neuronalen Netzen und Evolutionaeren Algorithmen. Besondere Schwerpunkte sind automatisierungstechnische Anwendungen, z.B. in der Verfahrenstechnik, Enegietechnik, Kfz-Technik, Robotik und Medizintechnik, aber auch Loesungen in anderen Problemgebieten (z.B. Data Mining fuer technische und nichttechnische Anwendungen) sind von Interesse. (orig.)
How to combine probabilistic and fuzzy uncertainties in fuzzy control
Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert
1991-01-01
Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.
Document Retrieval Using A Fuzzy Knowledge-Based System
Subramanian, Viswanath; Biswas, Gautam; Bezdek, James C.
1986-03-01
This paper presents the design and development of a prototype document retrieval system using a knowledge-based systems approach. Both the domain-specific knowledge base and the inferencing schemes are based on a fuzzy set theoretic framework. A query in natural language represents a request to retrieve a relevant subset of documents from a document base. Such a query, which can include both fuzzy terms and fuzzy relational operators, is converted into an unambiguous intermediate form by a natural language interface. Concepts that describe domain topics and the relationships between concepts, such as the synonym relation and the implication relation between a general concept and more specific concepts, have been captured in a knowledge base. The knowledge base enables the system to emulate the reasoning process followed by an expert, such as a librarian, in understanding and reformulating user queries. The retrieval mechanism processes the query in two steps. First it produces a pruned list of documents pertinent to the query. Second, it uses an evidence combination scheme to compute a degree of support between the query and individual documents produced in step one. The front-end component of the system then presents a set of document citations to the user in ranked order as an answer to the information request.
Expert System Detects Power-Distribution Faults
Walters, Jerry L.; Quinn, Todd M.
1994-01-01
Autonomous Power Expert (APEX) computer program is prototype expert-system program detecting faults in electrical-power-distribution system. Assists human operators in diagnosing faults and deciding what adjustments or repairs needed for immediate recovery from faults or for maintenance to correct initially nonthreatening conditions that could develop into faults. Written in Lisp.
Design of a Recruiter Expert System
1989-03-01
expert system was designed using these characteristics and the minimum requirements for assignment to recruiting duty given in the Navy’s Enlisted...Transfer Manual. A recommended Command Officer’s Screening Form was designed that will have all the data needed to be placed into the expert system . Recommendations
Robust control for a class of uncertain switched fuzzy systems
Institute of Scientific and Technical Information of China (English)
Hong YANG; Jun ZHAO
2007-01-01
A model of uncertain switched fuzzy systems whose subsystems are uncertain fuzzy systems is presented.Robust controllers for a class of switched fuzzy systems are designed by using the Lyapunov function method. Stability conditions for global asymptotic stability are developed and a switching strategy is proposed. An example shows the effectiveness of the method.
Expert Systems in Civil Engineering.
1986-01-01
preparation for the next evaluation. Zozaya- Gorostiza and Hendrickson (18,p.4) allude to the importance of this for sensitivity analysis (i.e.- modifying...Intellignce, 2nd ed., Addison-Wesley Publishing Company, Inc., Reading,1984. Zozaya- Gorostiza , Carlos and Chris Hendrickson, An Expert 8ystem for
Expert system for traffic signal setting assistance
Energy Technology Data Exchange (ETDEWEB)
Zozaya-Gorostiza, C.; Hendrickson, C.
1987-03-01
An experimental knowledge-based expert system to assist in traffic signal setting for isolated intersections is presented. In contrast to existing computer aids, the system can be applied to intersections of highly irregular geometries. Algorithmic processes to evaluate signal settings and decision tables to identify traffic flow conflicts are invoked by the expert system; phase distribution of flows is performed by applying heuristic rules. The system was written in the OPS5 export system environment. Advantages and disadvantages of the expert system programming approach relative to conventional algorithmic processes in the traffic engineering domain are described.
EXPERT SYSTEMS - DEVELOPMENT OF AGRICULTURAL INSURANCE TOOL
2013-01-01
Because of the fact that specialty agricultural assistance is not always available when the farmers need it, we identified expert systems as a strong instrument with an extended potential in agriculture. This started to grow in scale recently, including all socially-economic activity fields, having the role of collecting data regarding different aspects from human experts with the purpose of assisting the user in the necessary steps for solving problems, at the performance level of the expert...
Z Number Based Fuzzy Inference System for Dynamic Plant Control
Directory of Open Access Journals (Sweden)
Rahib H. Abiyev
2016-01-01
Full Text Available Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.
Howard University Energy Expert Systems Institute Summer Program (EESI)
Momoh, James A.; Chuku, Arunsi; Abban, Joseph
1996-01-01
Howard University, under the auspices of the Center for Energy Systems and Controls runs the Energy Expert Systems Institute (EESI) summer outreach program for high school/pre-college minority students. The main objectives are to introduce precollege minority students to research in the power industry using modern state-of-the-art technology such as Expert Systems, Fuzzy Logic and Artificial Neural Networks; to involve minority students in space power management, systems and failure diagnosis; to generate interest in career options in electrical engineering; and to experience problem-solving in a teamwork environment consisting of faculty, senior research associates and graduate students. For five weeks the students are exposed not only to the exciting experience of college life, but also to the inspiring field of engineering, especially electrical engineering. The program consists of lectures in the fundamentals of engineering, mathematics, communication skills and computer skills. The projects are divided into mini and major. Topics for the 1995 mini projects were Expert Systems for the Electric Bus and Breast Cancer Detection. Topics on the major projects include Hybrid Electric Vehicle, Solar Dynamics and Distribution Automation. On the final day, designated as 'EESI Day' the students did oral presentations of their projects and prizes were awarded to the best group. The program began in the summer of 1993. The reaction from the students has been very positive. The program also arranges field trips to special places of interest such as the NASA Goddard Space Center.
An Expert System for Designing Fire Prescriptions
Elizabeth Reinhardt
1987-01-01
Managers use prescribed fire to accomplish a variety of resource objectives. The knowledge needed to design successful prescriptions is both quantitative and qualitative. Some of it is available through publications and computer programs, but much of the knowledge of expert practitioners has never been collected or published. An expert system being developed at the,...
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
Abraham, Ajith
2004-01-01
Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN a...
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Directory of Open Access Journals (Sweden)
P. O. Adebayo
2015-01-01
Full Text Available This paper depicts adaptation of expert systems technology using fuzzy logic to handle qualitative and uncertain facts in the decision making process. Over the years, performance evaluations of students are based on qualitative facts, which are now becoming numerically inestimable as a result of uncertainty factors. Through fuzzy logic the qualitative terms like; low, medium and high; low, moderate and high were numerically weighted during the final decision making on students’ performance. The key parameters were given weights according to their priorities through mapping of numeric results from uncertain knowledge. Mathematical formulae were applied to calculate the numeric results at the final stage. In this way, the developed fuzzy expert system was demonstrated to be an effective tool for evaluating the performances of candidates seeking for admission into Nigeria tertiary institutions. This may also be adopted as a useful tool by stakeholders in government and Industry to predict the standard and long term expectations in the nation-building enterprise.
Fuzzy Optimization and Normal Simulation for Solving Fuzzy Web Queuing System Problems
Directory of Open Access Journals (Sweden)
Xidong Zheng
2005-04-01
Full Text Available In this paper, we use both fuzzy optimization and normal simulation methods to solve fuzzy web planning model problems, which are queuing system problems for designing web servers. We apply fuzzy probabilities to the queuing system models with customers arrival rate l and servers?service rate m, and then compute fuzzy system performance variables, including Utilization, Number (of requests in the System, Throughput, and Response Time. For the fuzzy optimization method, we apply two-step calculation, first use fuzzy calculation to get the maximum and minimum values of fuzzy steady state probabilities, and then we compute the fuzzy system performance variables. For the simulation method, we use one-step normal queuing theory to simulate the whole system performance and its variables. We deal with queuing systems with a single server and multiple servers?cases, and compare the results of these two cases, giving a mathematical explanation of the difference. Keywords: Fuzzy Optimization, Normal Simulation, Queuing Theory, Web Planning Model.
Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach
Directory of Open Access Journals (Sweden)
Rana Dinesh Singh
2015-01-01
Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.
Distributed intrusion detection system based on fuzzy rules
Qiao, Peili; Su, Jie; Liu, Yahui
2006-04-01
Computational Intelligence is the theory and method solving problems by simulating the intelligence of human using computer and it is the development of Artificial Intelligence. Fuzzy Technique is one of the most important theories of computational Intelligence. Genetic Fuzzy Technique and Neuro-Fuzzy Technique are the combination of Fuzzy Technique and novel techniques. This paper gives a distributed intrusion detection system based on fuzzy rules that has the characters of distributed parallel processing, self-organization, self-learning and self-adaptation by the using of Neuro-Fuzzy Technique and Genetic Fuzzy Technique. Specially, fuzzy decision technique can be used to reduce false detection. The results of the simulation experiment show that this intrusion detection system model has the characteristics of distributed, error tolerance, dynamic learning, and adaptation. It solves the problem of low identifying rate to new attacks and hidden attacks. The false detection rate is low. This approach is efficient to the distributed intrusion detection.
Expert systems in power substation operation and control
Energy Technology Data Exchange (ETDEWEB)
Ribeiro, Guilherme Moutinho; Lambert-Torres, Germano; Silva, Alexandre P. Alves da [Escola Federal de Engenharia de Itajuba, MG (Brazil)
1994-12-31
With digital technology being increasingly adopted in power substations (SE), perspectives are created for integration of supervision, control and protection systems in addition to making its process automation feasible. Once a SE is digitalized, the systems which previously were implemented physically will be able to be implemented by means of computer programs (software), allowing the expansion of its scope of operation. Studies and research performed at the international level have pointed to the utilization of Expert Systems (ES) as a more suitable alternative for representation and solution of the operation and control problems, especially those which do not have an established theory, which are provided with diagnosis characteristics and that associate themselves with fuzzy data and information. (author) 4 refs., 3 figs.
Fuzzy Sliding Mode Control for Discrete Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
F.Qiao.Q.M.Zhu; A.Winfield; C.Melhuish
2003-01-01
Sliding mode control is introduced into classical model free fuzzy logic control for discrete time nonlinear systems with uncertainty to the design of a novel fuzzy sliding mode control to meet the requirement of necessary and sufficient reaching conditions of sliding mode control. The simulation results show that the proposed controller outperforms the original fuzzy sliding mode controller and the classical fuzzy logic controller in stability, convergence and robustness.
GA-ANFIS Expert System Prototype for Prediction of Dermatological Diseases.
Begic Fazlic, Lejla; Avdagic, Korana; Omanovic, Samir
2015-01-01
This paper presents novel GA-ANFIS expert system prototype for dermatological disease detection by using dermatological features and diagnoses collected in real conditions. Nine dermatological features are used as inputs to classifiers that are based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. After that, they are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validation of the novel GA-ANFIS system approach is performed in MATLAB environment by using validation set of data. Some conclusions concerning the impacts of features on the detection of dermatological diseases were obtained through analysis of the GA-ANFIS. We compared GA-ANFIS and ANFIS results. The results confirmed that the proposed GA-ANFIS model achieved accuracy rates which are higher than the ones we got by ANFIS model.
Prediction of Heart Attack Risk Using GA-ANFIS Expert System Prototype.
Begic Fazlic, Lejla; Avdagic, Aja; Besic, Ingmar
2015-01-01
The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been used as inputs to classifiers being based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. They are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validating of the novel GA-ANFIS system approach is performed in MATLAB environment. We compared GA-ANFIS and ANFIS results. The proposed GA-ANFIS model with the predicted value technique is more efficient when diagnosis of heart disease is concerned, as well the earlier method we got by ANFIS model.
Directory of Open Access Journals (Sweden)
K. A. Halim
2011-01-01
Full Text Available In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models.
Jess, the Java expert system shell
Energy Technology Data Exchange (ETDEWEB)
Friedman-Hill, E.J.
1997-11-01
This report describes Jess, a clone of the popular CLIPS expert system shell written entirely in Java. Jess supports the development of rule-based expert systems which can be tightly coupled to code written in the powerful, portable Java language. The syntax of the Jess language is discussed, and a comprehensive list of supported functions is presented. A guide to extending Jess by writing Java code is also included.
Expert system interaction with existing analysis codes
Energy Technology Data Exchange (ETDEWEB)
Ransom, V.H.; Fink, R.K.; Bertch, W.J.; Callow, R.A.
1986-01-01
Coupling expert systems with existing engineering analysis codes is a promising area in the field of artificial intelligence. The added intelligence can provide for easier and less costly use of the code and also reduce the potential for code misuse. This paper will discuss the methods available to allow interaction between an expert system and a large analysis code running on a mainframe. Concluding remarks will identify potential areas of expert system application with specific areas that are being considered in a current research program. The difficulty of interaction between an analysis code and an expert system is due to the incompatibility between the FORTRAN environment used for the analysis code and the AI environment used for the expert system. Three methods, excluding file transfer techniques, are discussed to help overcome this incompatibility. The first method is linking the FORTRAN routines to the LISP environment on the same computer. Various LISP dialects available on mainframes and their interlanguage communication capabilities are discussed. The second method involves network interaction between a LISP machine and a mainframe computer. Comparisons between the linking method and networking are noted. The third method involves the use of an expert system tool that is campatible with a FORTRAN environment. Several available tools are discussed. With the interaction methods identified, several potential application areas are considered. Selection of the specific areas that will be developed for the pilot project and applied to a thermal-hydraulic energy analysis code are noted.
Directory of Open Access Journals (Sweden)
A. Karimi Dizicheh
2016-03-01
Full Text Available In this paper, we firstly introduce system of first order fuzzy differential equations. Then, we convert the problem to two crisp systems of first order differential equations. For numerical aspects, we apply exponentially fitted Runge Kutta method to solve the fuzzy problems. We solve some well-known examples in order to demonstrate the applicability and accuracy of results.
Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.
Mansoor Baig, Mirza; Gholamhosseini, Hamid; Harrison, Michael J
2013-07-01
Anaesthesia monitoring involves critical diagnostic tasks carried out amongst lots of distractions. Computers are capable of handling large amounts of data at high speed and therefore decision support systems and expert systems are now capable of processing many signals simultaneously in real time. We have developed two fuzzy logic based anaesthesia monitoring systems; a real time smart anaesthesia alarm system (RT-SAAM) and fuzzy logic monitoring system-2 (FLMS-2), an updated version of FLMS for the detection of absolute hypovolaemia. This paper presents the design aspects of these two systems which employ fuzzy logic techniques to detect absolute hypovolaemia, and compares their performances in terms of usability and acceptability. The interpretation of these two systems of absolute hypovolaemia was compared with clinicians' assessments using Kappa analysis, RT-SAAM K=0.62, FLMS-2 K=0.75; an improvement in performance by FLMS-2.
Sattler, Claudia; Stachow, Ulrich; Berger, Gert
2012-03-01
The study presented here describes a modeling approach for the ex-ante assessment of farming practices with respect to their risk for several single-species biodiversity indicators. The approach is based on fuzzy-logic techniques and, thus, is tolerant to the inclusion of sources of uncertain knowledge, such as expert judgment into the assessment. The result of the assessment is a so-called Index of Suitability (IS) for the five selected biotic indicators calculated per farming practice. Results of IS values are presented for the comparison of crops and for the comparison of several production alternatives per crop (e.g., organic vs. integrated farming, mineral vs. organic fertilization, and reduced vs. plow tillage). Altogether, the modeled results show that the different farming practices can greatly differ in terms of their suitability for the different biotic indicators and that the farmer has a certain scope of flexibility in opting for a farming practice that is more in favor of biodiversity conservation. Thus, the approach is apt to identify farming practices that contribute to biodiversity conservation and, moreover, enables the identification of farming practices that are suitable with respect to more than one biotic indicator.
Fuzzy Logic System for Slope Stability Prediction
Directory of Open Access Journals (Sweden)
Tarig Mohamed
2012-01-01
Full Text Available The main goal of this research is to predict the stability of slopes using fuzzy logic system. GeoStudio, a commercially available software was used to compute safety factors for various designs of slope. The general formulation of the software could analyze slope stability using various methods of analysis i.e. Morgenstern-Price, Janbu, Bishop and Ordinary to calculate the safety factors. After analyzing, fuzzy logic was used to predict the slope stability. Fuzzy logic is based on natural language and conceptually easy to understand, flexible, tolerant of imprecise data and able to model nonlinear functions of arbitrary complexity. Several important parameters such as height of slope, unit weight of slope material, angle of slope, coefficient of cohesion and internal angle of friction were used as the input parameters, while the factor of safety was the output parameter. A model to test the stability of the slope was generated from the calculated data. This model presented a relationship between input parameters and stability of the slopes. Results showed that the prediction using fuzzy logic was accurate and close to the target data.
Computer-Assisted Diagnosis in Reading: An Expert Systems Approach.
McEneaney, John E.
1992-01-01
Introduces some basic concepts on which expert systems are based. Considers how expert systems might be productively applied in education. Describes an experimental expert system with applications in reading diagnosis and teacher training. (SR)
Optical Design Using an Expert System
Energy Technology Data Exchange (ETDEWEB)
Lerner, S A
2003-08-01
We present, as a different perspective on optimization, an expert system for optimization of optical systems that can be used in conjunction with damped least squared methods to find minima for specific design forms. Expert system optimization differs from global optimization in that it preserves the basic structure of the optical system and limits its search for a minima to a relatively small portion of the design space. In general, the high density of local minima obscures the general trend of the merit function in the region of interest for systems with a large number of variables and constraints. Surprisingly, there may be a potential decrease of an order a magnitude in the merit function for a region of solution space. While global optimization is well-suited to identifying design forms of interest, expert system optimization can be used for in-depth optimization of such forms. An expert system based upon such techniques was used to obtain the winning entry for the 2002 IODC lens design problem. The expert system used is discussed along with other design examples.
Living Expert System (LEXSYS). Volume 7
1989-05-15
Partial contents: Army: LEXSYS--What Its All About; Summary of Team Meeting; How Can We Build a Non-Maintenance Intensive Expert Data Base; Cost Data for LEXSYS Prototype Issues; Search for the Perfect Rolodex; Reaching Decision Points in LEXSYS Subnets; An Architecture for a Prototype Net?; Helicopter FLOT Operations; Battlestaff Integration; Hardware Availability. Keywords: Dialogue; Conversation; LEXSYS (Living Expert System ); Decision making; Decision aids.
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Mahdaoui, Rafik; Mouss, Mohamed Djamel; Chouhal, Ouahiba
2011-01-01
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures....
Recommendation System Based on Fuzzy Cognitive Map
Directory of Open Access Journals (Sweden)
Wei Liu
2014-07-01
Full Text Available With the increase of data volume and visitor volume, the website faces great challenge in the environment of network. How to know the users’ requirements rapidly and effectively and recommend the required information to the user becomes the research direction of all websites. The researchers of recommendation system propose a series of recommendation system models and algorithms for the user. The common challenge faced by these algorithms is how to judge the user intention and recommend the relevant content by little user action. The paper proposes the user situation awareness and information recommendation system based on fuzzy clustering analysis and fuzzy cognitive maps, and verifies the validity of the algorithm by the application to recommendation site of academic thesis.
Fuzzy controller for an uncertain dynamical system
DEFF Research Database (Denmark)
Kulczycki, P.; Wisniewski, Rafal
2002-01-01
The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters. The met......The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters....... The methodology proposed in this work may be easily adopted to other modeling uncertainties of mechanical systems, e.g. motion resistance....
Fuzzy Logic Based Power System Contingency Ranking
Directory of Open Access Journals (Sweden)
A. Y. Abdelaziz
2013-02-01
Full Text Available Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents maximum loadability identification of a load bus in a power transmission network. In this study, Fast Voltage Stability Index (FVSI is utilized as the indicator of the maximum loadability termed as Qmax. In this technique, reactive power loading will be increased gradually at particular load bus until the FVSI reaches close to unity. Therefore, a critical value of FVSI was set as the maximum loadability point. This value ensures the system from entering voltage-collapse region. The main purpose in the maximum loadability assessment is to plan for the maximum allowable load value to avoid voltage collapse; which is important in power system planning risk assessment.The most important task in security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and ranks them according to their severity. The condition of voltage stability in a power system can be characterized by the use of voltage stability indices. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F and bus Voltage Magnitude (VM of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provides the comparison of ranking obtained with FVSI method.
Expert system for web based collaborative CAE
Hou, Liang; Lin, Zusheng
2006-11-01
An expert system for web based collaborative CAE was developed based on knowledge engineering, relational database and commercial FEA (Finite element analysis) software. The architecture of the system was illustrated. In this system, the experts' experiences, theories and typical examples and other related knowledge, which will be used in the stage of pre-process in FEA, were categorized into analysis process and object knowledge. Then, the integrated knowledge model based on object-oriented method and rule based method was described. The integrated reasoning process based on CBR (case based reasoning) and rule based reasoning was presented. Finally, the analysis process of this expert system in web based CAE application was illustrated, and an analysis example of a machine tool's column was illustrated to prove the validity of the system.
Feasibility of physician-developed expert systems.
Tuhrim, S; Reggia, J A
1986-01-01
The authors developed an experimental domain-independent "expert system generator" intended for direct use by physicians. They then undertook a four-year study to determine whether physicians could use such a system effectively. During this period they taught the use of the expert system generator to 70 medical students, who utilized it to build two small medical expert systems. At the conclusion of the course, students were examined on decision-making concepts and completed anonymous questionnaires. Performance scores, a composite of test and project grades, were calculated for each student. There was no significant association between previous computer experience and performance score. Thirty-two of 47 students responding felt the expert system generator was easy to use; 15 felt it was of moderate difficulty. Forty-three of 47 thought it a useful teaching aid. These data support the conclusion that physicians can learn to use domain-independent software to implement medical expert systems directly, without a knowledge engineer as an intermediary.
On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems
Directory of Open Access Journals (Sweden)
M. S. N. Murty
2008-04-01
Full Text Available We provide a way to combine matrix Lyapunov systems with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix Lyapunov system, which can be regarded as a new approach to intelligent control. First, we study the controllability property of the fuzzy dynamical matrix Lyapunov system and provide a sufficient condition for its controllability with the use of fuzzy rule base. The significance of our result is that given a deterministic system and a fuzzy state with rule base, we can determine the rule base for the control. Further, we discuss the concept of observability and give a sufficient condition for the system to be observable. The advantage of our result is that we can determine the rule base for the initial value without solving the system.
Energy Technology Data Exchange (ETDEWEB)
Castro, Antonio Orestes de Salvo [PETROBRAS, Rio de Janeiro, RJ (Brazil); Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)
2004-07-01
The hydraulic fracture operation is wide used to increase the oil wells production and to reduce formation damage. Reservoir studies and engineer analysis are made to select the wells for this kind of operation. As the reservoir parameters have some diffuses characteristics, Fuzzy Inference Systems (SIF) have been tested for this selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for hydraulic Fracture well selection, with knowledge acquisition from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, in despite of the genetic fuzzy system would be a younger process, it got better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variables linguistic values. (author)
Generating Interpretable Fuzzy Systems for Classification Problems
Directory of Open Access Journals (Sweden)
Juan A. Contreras-Montes
2009-12-01
Full Text Available This paper presents a new method to generate interpretable fuzzy systems from training data to deal with classification problems. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in another method. Singleton consequents are generated form the projection of the modal values of each triangular membership function into the output space. Least square method is used to adjust the consequents. The proposed method gets a higher average classification accuracy rate than the existing methods with a reduced number of rules andparameters and without sacrificing the fuzzy system interpretability. The proposed approach is applied to two classical classification problems: Iris data and the Wisconsin Breast Cancer classification problem.
Dietitians: experts about food systems?
Klitzke, C
1997-10-01
Dietary advice that promotes optimal nutrition presupposes adequate land for growing food, environmental quality conducive to food production, stable government, a strong economy, and community food security (or a reliable transportation system). Without these conditions, recommendations for optimal nutrition become moot. Our profession will be strengthened as we develop a broad knowledge of our entire food system.
Porting a Mental Expert System to a Mainstream Programming Environment
Jao, Chiang S.; Hier, Daniel B.; Dollear, Winifred; Fu, Wenying
2001-01-01
Expert systems are increasingly being applied to problems in medical diagnosis and treatment. Initial medical expert systems were programmed in specialized “expert system” shell programming environments. As the power of mainstream programming languages has increased, it has become possible to implement medical expert systems within these mainstream languages. We originally implemented an expert system to record and score the mental status examination utilizing a specialized expert system prog...
The diagnosis of microcytic anemia by a rule-based expert system using VP-Expert.
O'Connor, M L; McKinney, T
1989-09-01
We describe our experience in creating a rule-based expert system for the interpretation of microcytic anemia using the expert system development tool, VP-Expert, running on an IBM personal computer. VP-Expert processes data (complete blood cell count results, age, and sex) according to a set of user-written logic rules (our program) to reach conclusions as to the following causes of microcytic anemia: alpha- and beta-thalassemia trait, iron deficiency, and anemia of chronic disease. Our expert system was tested using previously interpreted complete blood cell count data. In most instances, there was good agreement between the expert system and its pathologist-author, but many discrepancies were found in the interpretation of anemia of chronic disease. We conclude that VP-Expert has a useful level of power and flexibility, yet is simple enough that individuals with modest programming experience can create their own expert systems. Limitations of such expert systems are discussed.
Hybrid Expert Systems In Image Analysis
Dixon, Mark J.; Gregory, Paul J.
1987-04-01
Vision systems capable of inspecting industrial components and assemblies have a large potential market if they can be easily programmed and produced quickly. Currently, vision application software written in conventional high-level languages such as C or Pascal are produced by experts in program design, image analysis, and process control. Applications written this way are difficult to maintain and modify. Unless other similar inspection problems can be found, the final program is essentially one-off redundant code. A general-purpose vision system targeted for the Visual Machines Ltd. C-VAS 3000 image processing workstation, is described which will make writing image analysis software accessible to the non-expert both in programming computers and image analysis. A significant reduction in the effort required to produce vision systems, will be gained through a graphically-driven interactive application generator. Finally, an Expert System will be layered on top to guide the naive user through the process of generating an application.
Fuzzy system dynamics and optimization with application to manpower systems
Directory of Open Access Journals (Sweden)
C. Mbohwa
2012-10-01
Full Text Available The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative. In this frame of mind, a fuzzy systems dynamics modelling approach is proposed to enable the policy maker to develop reliable dynamic policies relating recruitment, training, and available skills, from a systems perspective. It is anticipated in this study that fuzzy system dynamics and optimization approach would help organizations to design effective manpower policies and strategies.
FUZZY ALGEBRA IN TRIANGULAR NORM SYSTEM
Institute of Scientific and Technical Information of China (English)
宋晓秋; 潘志
1994-01-01
Triangular norm is a powerful tool in the theory research and application development of fuzzy sets. In this paper, using the triangular norm, we introduce some concepts such as fuzzy algebra, fuzzy o algebra and fuzzy monotone class, and discuss the relations among them, obtaining the following main conclusions.
Santos, Sandra A; de Lima, Helano Póvoas; Massruhá, Silvia M F S; de Abreu, Urbano G P; Tomás, Walfrido M; Salis, Suzana M; Cardoso, Evaldo L; de Oliveira, Márcia Divina; Soares, Márcia Toffani S; Dos Santos, Antônio; de Oliveira, Luiz Orcírio F; Calheiros, Débora F; Crispim, Sandra M A; Soriano, Balbina M A; Amâncio, Christiane O G; Nunes, Alessandro Pacheco; Pellegrin, Luiz Alberto
2017-08-01
One of the most relevant issues in discussion worldwide nowadays is the concept of sustainability. However, sustainability assessment is a difficult task due to the complexity of factors involved in the natural world added to the human interference. In order to assess the sustainability of beef ranching in complex and uncertain tropical environment systems this paper describes a decision support system based on fuzzy rule-approach, the Sustainable Pantanal Ranch (SPR). This tool was built by a set of measurements and indicators integrated by fuzzy logic to evaluate the attributes of the three dimensions of sustainability. Indicators and decision rules, as well as scenario evaluations, were obtained from workshops involving multi-disciplinary team of experts. A Fuzzy Rule-Based System (FRBS) was developed to each attribute, dimension and general index. The essential parts of the FRBS are the knowledge database, rules and the inference engine. The FuzzyGen and WebFuzzy tools were developed to support the FRBS and both showed efficiency and low cost for digital applications. The results of each attribute, dimension and index were presented as radar graphs, showing the individual value (0-10) of each indicator. In the validation process using the WebFuzzy, different combinations of indicators were made for each attribute index to show the corresponding output, and which confirm the feasibility and usability of the tool. Copyright © 2017 Elsevier Ltd. All rights reserved.
Database and Expert Systems Applications
DEFF Research Database (Denmark)
Viborg Andersen, Kim; Debenham, John; Wagner, Roland
submissions. The papers are organized in topical sections on workflow automation, database queries, data classification and recommendation systems, information retrieval in multimedia databases, Web applications, implementational aspects of databases, multimedia databases, XML processing, security, XML...... schemata, query evaluation, semantic processing, information retrieval, temporal and spatial databases, querying XML, organisational aspects of databases, natural language processing, ontologies, Web data extraction, semantic Web, data stream management, data extraction, distributed database systems...
Fuzzy Rule Base System for Software Classification
Directory of Open Access Journals (Sweden)
Adnan Shaout
2013-07-01
Full Text Available Given the central role that software development plays in the delivery and application of informationtechnology, managers have been focusing on process improvement in the software development area. Thisimprovement has increased the demand for software measures, or metrics to manage the process. Thismetrics provide a quantitative basis for the development and validation of models during the softwaredevelopment process. In this paper a fuzzy rule-based system will be developed to classify java applicationsusing object oriented metrics. The system will contain the following features:Automated method to extract the OO metrics from the source code,Default/base set of rules that can be easily configured via XML file so companies, developers, teamleaders,etc, can modify the set of rules according to their needs,Implementation of a framework so new metrics, fuzzy sets and fuzzy rules can be added or removeddepending on the needs of the end user,General classification of the software application and fine-grained classification of the java classesbased on OO metrics, andTwo interfaces are provided for the system: GUI and command.
Uncertainty in Interval Type-2 Fuzzy Systems
Directory of Open Access Journals (Sweden)
Sadegh Aminifar
2013-01-01
Full Text Available This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions differentiate from T1MFs (type-1 membership functions by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs. Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is introduced to observe the effect of uncertainty on the output. The theorem of uncertainty avoidance is presented for describing the role of uncertainty in interval type-2 fuzzy systems (IT2FSs. Another objective of this paper is to derive a novel uncertainty measure for IT2MFs with lower complexity and clearer presentation. Finally, for proving the affectivity of novel interpretation of uncertainty in IT2FSs, several investigations are done.
Enhancing an adaptive e-learning system with didactic test assessment using an expert system
Bradáč, Vladimír; Kostolányová, Kateřina
2017-07-01
The paper deals with a follow-up research on intelligent tutoring systems that were studied in authors' previous papers from the point of view of describing their advantages. In this paper, the authors make use of the fuzzy logic expert system, which assesses student's knowledge, and integrate it into the intelligent tutoring system called Barborka. The goal is to create an even more personal student's study plan, which is tailored both to student's sensory/learning preferences and the level of knowledge of the given subject.
Genetic fuzzy system modeling and simulation of vascular behaviour
DEFF Research Database (Denmark)
Tang, Jiaowei; Boonen, Harrie C.M.
and find the optimal parameters in a Fuzzy Control set that can control the fluctuation of physical features in a blood vessel, based on experimental data (training data). Our solution is to create chromosomes or individuals composed of a sequence of parameters in the fuzzy system and find the best...... chromosome or individual to define the fuzzy system. The model is implemented by combining the Matlab Genetic algorithm and Fuzzy system toolboxes, respectively. To test the performance of this method, experimental data sets about calculated pressure change in different blood vessels after several chemical...... treatments are chosen as training and testing data sets. In the simulation, the fuzzy control system is trained by pressure data of one blood vessel and tested with pressure data of other blood vessels. Results: Right now, some rough results show that trained fuzzy control system can be used to predict...
Laserjet Printer Troubleshooting Expert System
African Journals Online (AJOL)
SOFTLINKS DIGITAL
2 and Umoh, E. E.. 3. 1Department of Computer Science, Cross River University of Technology, Calabar, Cross River State. ... a rule-base, an inference engine, and a knowledge editor interface. The system is .... It is the brain logical reasoning ...
ROSIE: A Programming Environment for Expert Systems
1985-10-01
ence on Artificial Inteligence , Tbilisi, USSR, 1975. Fain, J., D. Gorlin, F. Hayes-Roth, S. Rosenschein, H. Sowizral, and D. Waterman, The ROSIE Language...gramming environment for artificial intelligence (AI) applications. It provides particular support for designing expert systems, systems that embody
Indian Academy of Sciences (India)
Diptiranjan Behera; S Chakraverty
2015-02-01
This paper proposes two new methods to solve fully fuzzy system of linear equations. The fuzzy system has been converted to a crisp system of linear equations by using single and double parametric form of fuzzy numbers to obtain the non-negative solution. Double parametric form of fuzzy numbers is defined and applied for the first time in this paper for the present analysis. Using single parametric form, the $n \\times n$ fully fuzzy system of linear equations have been converted to a $2n \\times 2n$ crisp system of linear equations. On the other hand, double parametric form of fuzzy numbers converts the n×n fully fuzzy system of linear equations to a crisp system of same order. Triangular and trapezoidal convex normalized fuzzy sets are used for the present analysis. Known example problems are solved to illustrate the efficacy and reliability of the proposed methods.
Fuzzy Knowledge-based System with Uncertainty for Tropical Infectious Disease Diagnosis
Directory of Open Access Journals (Sweden)
Putu Manik Prihatini
2012-07-01
Full Text Available On the development of medical knowledge-based system, the inability of a patient in a complaint must be dealt with by the fuzzy logic method, while the inability of an expert in defining the relationship between the symptoms of the disease can be treated with certainty factor method. In this study, both methods were combined to make diagnosis of tropical infectious diseases. Knowledge acquired from medical specialist in internal medicine, with produce fact, a crisp and fuzzy symptoms, and rule with the certainty value of the specialist. Reasoning process starts from the implication, decomposition, defuzzification and certainty factor calculation. System developed on web based platform and provide a workplace, explanation facility and knowledge improvement. System testing is done to compare the results of specialist diagnosis and system diagnosis, which results of testing show the system, has similarity with the expert at 91.07%.
Spacecraft command and control using expert systems
Norcross, Scott; Grieser, William H.
1994-11-01
This paper describes a product called the Intelligent Mission Toolkit (IMT), which was created to meet the changing demands of the spacecraft command and control market. IMT is a command and control system built upon an expert system. Its primary functions are to send commands to the spacecraft and process telemetry data received from the spacecraft. It also controls the ground equipment used to support the system, such as encryption gear, and telemetry front-end equipment. Add-on modules allow IMT to control antennas and antenna interface equipment. The design philosophy for IMT is to utilize available commercial products wherever possible. IMT utilizes Gensym's G2 Real-time Expert System as the core of the system. G2 is responsible for overall system control, spacecraft commanding control, and spacecraft telemetry analysis and display. Other commercial products incorporated into IMT include the SYBASE relational database management system and Loral Test and Integration Systems' System 500 for telemetry front-end processing.
An expert system for astronaut scientists
Young, L. R.
1991-01-01
A novel application of expert system technology is developed for real-time advice to an astronaut during the performance of a crew intensive experiment. The provision of an on-board computer expert, containing much of the reasoning base of the real Principal Investigator, will permit the astronaut to act more as a scientist co-worker in future Spacelab and Space Station missions. The long duration of flight increments and the large number of experiments envisioned for Space Station Freedom make the increase in astronaut productivity particularly valuable. A first version of the system was evaluated on the ground during the recent Spacelab SLS-1 flight.
Expert System Software Assistant for Payload Operations
Rogers, Mark N.
1997-01-01
The broad objective of this expert system software based application was to demonstrate the enhancements and cost savings that can be achieved through expert system software utilization in a spacecraft ground control center. Spacelab provided a valuable proving ground for this advanced software technology; a technology that will be exploited and expanded for future ISS operations. Our specific focus was on demonstrating payload cadre command and control efficiency improvements through the use of "smart" software which monitors flight telemetry, provides enhanced schematic-based data visualization, and performs advanced engineering data analysis.
Fuzzy Control Method with Application for Functional Neuromuscular Stimulation System
Institute of Scientific and Technical Information of China (English)
吴怀宇; 周兆英; 熊沈蜀
2001-01-01
A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physicalmultiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controllerwas developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angletrajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and aconventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulationresults demonstrated that the proposed fuzzy control method is more suitable for the physiologicalcharacteristics than conventional PID control. In particular, both the trajectory-following and the stability of theFNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trainsgenerated by the fuzzy controller were stable and smooth.``
Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan
2017-09-01
This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.
EXPERT SYSTEMS - DEVELOPMENT OF AGRICULTURAL INSURANCE TOOL
Directory of Open Access Journals (Sweden)
NAN Anca-Petruţa
2013-07-01
Full Text Available Because of the fact that specialty agricultural assistance is not always available when the farmers need it, we identified expert systems as a strong instrument with an extended potential in agriculture. This started to grow in scale recently, including all socially-economic activity fields, having the role of collecting data regarding different aspects from human experts with the purpose of assisting the user in the necessary steps for solving problems, at the performance level of the expert, making his acquired knowledge and experience available. We opted for a general presentation of the expert systems as well as their necessity, because, the solution to develop the agricultural system can come from artificial intelligence by implementing the expert systems in the field of agricultural insurance, promoting existing insurance products, farmers finding options in depending on their necessities and possibilities. The objective of this article consists of collecting data about different aspects about specific areas of interest of agricultural insurance, preparing the database, a conceptual presentation of a pilot version which will become constantly richer depending on the answers received from agricultural producers, with the clearest exposure of knowledgebase possible. We can justify picking this theme with the fact that even while agricultural insurance plays a very important role in agricultural development, the registered result got from them are modest, reason why solutions need to be found in the scope of developing the agricultural sector. The importance of this consists in the proposal of an immediate viable solution to correspond with the current necessities of agricultural producers and in the proposal of an innovative solution, namely the implementation of expert system in agricultural insurance as a way of promoting insurance products. Our research, even though it treats the subject at an conceptual level, it wants to undertake an
A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty.
Hossain, Mohammad Shahadat; Ahmed, Faisal; Fatema-Tuj-Johora; Andersson, Karl
2017-03-01
The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule based expert system.
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
Directory of Open Access Journals (Sweden)
Vasile MAZILESCU
2010-12-01
Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.
Output-back fuzzy logic systems and equivalence with feedback neural networks
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.
The Research of System Architecture in Expert System
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper illustrated the software architecture of our concretesafet y expert system. Three advanced technologies are proposed and have been applied to our expert system to greatly improve the intelligent level, which are human- compu ter interaction technology (conceptual model, dialogue management, interface ent ity and interface construct), intelligent agency user interface (IAUI) and compo nent technology. The i mportant character of the system architecture in our expert system is adapting a dvanced intelligent interface and scientific integration of various components d ifferent from common system architecture of expert system. Especially, in the in terface-friendly multimedia system intelligent interface is required.
A Formal Definition for Expert Systems used in Real-time Applications
Directory of Open Access Journals (Sweden)
Vasile MAZILESCU
2015-05-01
Full Text Available The present paper is situated on the grounds of research in the field of symbolic Artificial Intelligence systems, applied to the new Knowledge Management Systems. The basic feature of these systems is represented by the processing of the fuzzy knowledge involved in the synthesis of some decisions. The work reported in this paper serves to promote an Intelligent System that can operate in dynamic and uncertain environments based on a formal definition of an expert system. We can develop and justify thus a series of modelling and design techniques for Intelligent Knowledge Management Systems (IKMS, as well as methods for the analysis of expert systems performance, and, of a fuzzy expert system in particular, between which there are strong similarities. We will also outline a number of differences between conventional problem solving systems and IKMS, the links between expert systems and those of structural and functional planning, the analogy between the model of the problem or business process and the problem domain.
A proposed method for solving fuzzy system of linear equations.
Kargar, Reza; Allahviranloo, Tofigh; Rostami-Malkhalifeh, Mohsen; Jahanshaloo, Gholam Reza
2014-01-01
This paper proposes a new method for solving fuzzy system of linear equations with crisp coefficients matrix and fuzzy or interval right hand side. Some conditions for the existence of a fuzzy or interval solution of m × n linear system are derived and also a practical algorithm is introduced in detail. The method is based on linear programming problem. Finally the applicability of the proposed method is illustrated by some numerical examples.
REXS : A financial risk diagnostic expert system
Directory of Open Access Journals (Sweden)
W. Richter
2012-01-01
Full Text Available
ENGLISH ABSTRACT: Artificial intelligence techniques are rapidly emerging as important contributors to more effective management. One of the greatest growth areas probably lies in the use of Expert System methodology for supporting managerial decision processes.
Existing Decision Support Systems often attempt to apply analytical techniques in combination with traditional data access and retrieval functions. One of the problems usually encountered while developing such decision support systems is the need to transform an unstructured problem environment into a structured analytical model. Using an expert system approach to strategic decision making in such unstructured problem environments may provide significant advantages.
The financial Risk diagnostic EXpert System (REXS concentrates on Financial Risk Analysis. Based on a Forecasting Model the system will, with the support of several expert system knowledge bases, attempt to evaluate the financial risk of a business and provide guidelines for improvement.
AFRIKAANSE OPSOMMING: Tegnieke gebaseer op Kunsmatige Intelligensie toon tans die belofte om belangrike bydraes te maak tot meerBestaande Besluitsteunstelsels poog dikwels om analitiese tegnieke en lradisionele datatoegang- en onttrekkingsfunksies te kombineer. Een van die probleme wat gewoonlik ondervind word gedurende die ontwikkeling van '0 besluitsteunstelsel bestaan uit die behoefte om 'n ongestruktueerde probleemomgewing te transformeer na 'n gestruktueerde analitiese model. 'n Ekspertstelselbenadering lot strategiese besluitneming in 'n ongeSlruktureerde probleemomgewing mag betekenisvolle voordele inhou.
Die "financial Risk diagnostic EXpert System (REXS" konsentreer op fmansiele risiko-analise. Uitgaande vanaf 'n Vooruitskattingsmode~ en deur gebruik te maak van verskeie ekspertstelselkennisbasisse, poog die stelsel om die fmansiele risiko van 'n onderneming te evalueer en riglyne vir moontlike verbetering
Reliable fuzzy control with domain guaranteed cost for fuzzy systems with actuator failures
Institute of Scientific and Technical Information of China (English)
JIA Xinchun; ZHENG Nanning
2004-01-01
The reliable fuzzy control with guaranteed cost for T-S fuzzy systems with actuator failure is proposed in this paper. The cost function is a quadratic function with failure input. When the initial state of such systems is known, a design method of the reliable fuzzy controller with reliable guaranteed cost is presented, and the formula of the guaranteed cost is established. When the initial state of such systems is unknown but belongs to a known bounded closed domain, a notion of the reliable domain guaranteed cost (RDGC) for such systems is proposed. For two classes of initial state domain, polygon domain and ellipsoid domain, some design methods for reliable fuzzy controllers with the RDGC are provided. The efficiency of our design methods is finally verified by numerical design and simulation on the Rossler chaotic system.
Concept of an expert system for EQCreator
Schenk, Jiri; Telnarova, Zdenka; Habiballa, Hashim
2016-06-01
This article deals with the design of an ideal, and to some extent general, expert system for evaluation of randomly generated algebra with the help of EQCreator program, which is able to generate EQ of algebra. It was created for the purpose of future expansion of the program for the possibility of generating any algebra specified by the user.
Planning bioinformatics workflows using an expert system.
Chen, Xiaoling; Chang, Jeffrey T
2017-04-15
Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used. To automate the development of pipelines, we have investigated expert systems. We created the Bioinformatics ExperT SYstem (BETSY) that includes a knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce workflows. Currently, the knowledge base is populated with rules to analyze microarray and next generation sequencing data. We evaluated BETSY and found that it could generate workflows that reproduce and go beyond previously published bioinformatics results. Finally, a meta-investigation of the workflows generated from the knowledge base produced a quantitative measure of the technical burden imposed by each step of bioinformatics analyses, revealing the large number of steps devoted to the pre-processing of data. In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise. https://github.com/jefftc/changlab. jeffrey.t.chang@uth.tmc.edu.
Air Combat Maneuvering Expert System Trainer
1992-01-01
AL-TP-1 991-0058....... AD-A246 459 AIR COMBAT MANEUVERING EXPERT A SYSTEM TRAINER R M S Robert J. BechtelTI T Markt Technology, incorporated ’T R...would have to be established for each segment of pilot training. The success of the air intercept trainer (AT), which shares some features with ACMEST
Using Expert Systems To Build Cognitive Simulations.
Jonassen, David H.; Wang, Sherwood
2003-01-01
Cognitive simulations are runnable computer programs for modeling human cognitive activities. A case study is reported where expert systems were used as a formalism for modeling metacognitive processes in a seminar. Building cognitive simulations engages intensive introspection, ownership and meaning making in learners who build them. (Author/AEF)
Genesis of an Electronic Database Expert System.
Ma, Wei; Cole, Timothy W.
2000-01-01
Reports on the creation of a prototype, Web-based expert system that helps users better navigate library databases at the University of Illinois at Urbana-Champaign. Discusses concerns that gave rise to the project. Summarizes previous work/research and common approaches in academic libraries today. Describes plans for testing the prototype,…
Expert System Model for Educational Personnel Selection
Directory of Open Access Journals (Sweden)
Héctor A. Tabares-Ospina
2013-06-01
Full Text Available The staff selection is a difficult task due to the subjectivity that the evaluation means. This process can be complemented using a system to support decision. This paper presents the implementation of an expert system to systematize the selection process of professors. The management of software development is divided into 4 parts: requirements, design, implementation and commissioning. The proposed system models a specific knowledge through relationships between variables evidence and objective.
Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty
Starczewski, Janusz T
2013-01-01
This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory. In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...
FUZZY LOGIC MULTI-AGENT SYSTEM
Atef GHARBI; Ben Ahmed, Samir
2014-01-01
The paper deals with distributed planning in a Multi-Agent System (MAS) constituted by several intelligent agents each one has to interact with the other autonomous agents. The problem faced is how to ensure a distributed planning through the cooperation in our multi-agent system. To do so, we propose the use of fuzzy logic to represent the response of the agent in case of interaction with the other. Finally, we use JADE platform to create agents and ensure the communication be...
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not
Fuzzy Control of Chaotic System with Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang
2002-01-01
A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.
A FUZZY INFERENCE SYSTEM FOR ASSESSMENT OF THE SEVERITY OF THE PEPTIC ULCERS
Directory of Open Access Journals (Sweden)
Kianaz Rezaei
2014-05-01
Full Text Available Peptic ulcer disease is the most common ulcer of an area of the gastro- intestinal tract. The aim of this study is to utilize soft computing techniques to manage uncertainty and imprecision in measurements related to the size, shape of the abnormality. For this, we designed a fuzzy inference system (FIS which emulates the process of human experts in detection and analysis of the peptic ulcer. The proposed approach models the vagueness and uncertainty associated to measurements of small objects in low resolution images In this study, for the first time, we applied soft computing technique based upon fuzzy inference system (FIS for assessment of the severity of the peptic ulcer. Performance results reveal the FIS with maximum accuracy of 98.1%, which reveals superiority of the approach. The intelligent FIS system can help medical experts as a second reader for detection of the peptic ulcer in the decision making process and consequently, improves the treatment process.
Institute of Scientific and Technical Information of China (English)
马博; 倪红芳; 朱晓丽; 曾鸣
2013-01-01
Aiming at the deficiency of contingency cost estimation of project investment with current method, contingency cost estimation model is established by taking EHV infrastructure project for an example. The risks that may exist in the implementation process of EHV infrastructure projects are analyzed and classified. And then, fuzzy expert system is used to estimate the contingency cost of EHV infrastructure project. Example results show that the proposed model is feasible and effective for estimating contingency cost of EHV infrastructure project and the estimation error is within 10%.%针对工程项目投资中采用现有的方法估算不可预见费存在的不足,以超高压基建工程为例,构建了不可预见费估算理论模型,分析了超高压基建工程实施过程中存在的风险,并对各风险进行分类筛选,进而确定风险等级,再利用模糊专家系统估算工程的不可预见费.算例应用结果表明,基于风险分析和模糊专家系统来估算超高压基建工程不可预见费用的方法可行、有效,且估算误差均在10％以内.
Decision Making in Fuzzy Discrete Event Systems1.
Lin, F; Ying, H; Macarthur, R D; Cohn, J A; Barth-Jones, D; Crane, L R
2007-09-15
The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert's knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.
Zhao, Tao; Dian, Songyi
2017-09-01
This paper addresses a fuzzy dynamic output feedback H∞ control design problem for continuous-time nonlinear systems via T-S fuzzy model. The stability of the fuzzy closed-loop system which is formed by a T-S fuzzy model and a fuzzy dynamic output feedback H∞ controller connected in a closed loop is investigated with Lyapunov stability theory. The proposed fuzzy controller does not share the same membership functions and number of rules with T-S fuzzy systems, which can enhance design flexibility. A line-integral fuzzy Lyapunov function is utilized to derive the stability conditions in the form of linear matrix inequalities (LMIs). The boundary information of membership functions is considered in the stability analysis to reduce the conservativeness of the imperfect premise matching design technique. Two simulation examples are provided to demonstrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Mohammad Madadpour Inallou
2012-10-01
Full Text Available Expert Systems in Medicine is a collection, storage, retrieval, communication and processing of medical data for the purposes of interpretation, inference, decision-support, research and so other purposes in medicine. Expert System is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert. Expert systems provide expert advice and guidance in a wide variety of activities, from computer diagnosis to delicate medical surgery and so more other. CADIAG-2 is a well known rule-based medical expert system aimed at providing support in medical diagnose in the field of internal medicine. This paper employs CADIAG-2 fuzzy set theory and fuzzy logic to formalize medical entities and relationships and uses a large collection of IF-THEN rules to represent uncertain relationships between distinct medical entities. This paper is organized as follows: At first, the introduction and fundamental of CADIAG-2, in second step notation and preliminary definitions is regarded, and in third part the Algorithm will be consider, and the final step is discussed about satisfiablity and Unsatisfiable of CADIAG-2 sets.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
Kim, J; Kasabov, N
1999-11-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.
Expert Systems as a Mindtool To Facilitate Mental Model Learning.
Mason-Mason, Susan Dale; Tessmer, Martin A.
2000-01-01
This exploratory study investigated whether the process of constructing an expert system model promotes the formation of expert-like mental models. Discusses expert systems as mindtools, expert systems as learning tools, the assessment of mental models, results of pretests and posttests, and future research. (Contains 56 references.) (Author/LRW)
SELF LEARNING COMPUTER TROUBLESHOOTING EXPERT SYSTEM
Amanuel Ayde Ergado
2016-01-01
In computer domain the professionals were limited in number but the numbers of institutions looking for computer professionals were high. The aim of this study is developing self learning expert system which is providing troubleshooting information about problems occurred in the computer system for the information and communication technology technicians and computer users to solve problems effectively and efficiently to utilize computer and computer related resources. Domain know...
Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems.
Kumar, Abhishek; Sharma, Rajneesh
2017-03-01
We propose a fuzzy reinforcement learning (RL) based controller that generates a stable control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at lyapunov constraining the consequent part of fuzzy rules in a fuzzy RL setup. Ours is a first attempt at designing a linguistic RL controller with lyapunov constrained fuzzy consequents to progressively learn a stable optimal policy. The proposed controller does not need system model or desired response and can effectively handle disturbances in continuous state-action space problems. Proposed controller has been employed on the benchmark Inverted Pendulum (IP) and Rotational/Translational Proof-Mass Actuator (RTAC) control problems (with and without disturbances). Simulation results and comparison against a) baseline fuzzy Q learning, b) Lyapunov theory based Actor-Critic, and c) Lyapunov theory based Markov game controller, elucidate stability and viability of the proposed control scheme.
Minimal solution of general dual fuzzy linear systems
Energy Technology Data Exchange (ETDEWEB)
Abbasbandy, S. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin 34194-288 (Iran, Islamic Republic of)], E-mail: abbasbandy@yahoo.com; Otadi, M.; Mosleh, M. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Islamic Azad University, Firuozkooh Branch, Firuozkooh (Iran, Islamic Republic of)
2008-08-15
Fuzzy linear systems of equations, play a major role in several applications in various area such as engineering, physics and economics. In this paper, we investigate the existence of a minimal solution of general dual fuzzy linear equation systems. Two necessary and sufficient conditions for the minimal solution existence are given. Also, some examples in engineering and economic are considered.
Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications
Directory of Open Access Journals (Sweden)
Stefan Preitl
2006-07-01
Full Text Available The paper deals with both theoretical and application aspects concerningIterative Feedback Tuning (IFT algorithms in the design of a class of fuzzy controlsystems employing Mamdani-type PI-fuzzy controllers. The presentation is focused on twodegree-of-freedom fuzzy control system structures resulting in one design method. Thestability analysis approach based on Popov’s hyperstability theory solves the convergenceproblems associated to IFT algorithms. The suggested design method is validated by realtimeexperimental results for a fuzzy controlled nonlinear DC drive-type laboratoryequipment.
A Recursive Fuzzy System for Efficient Digital Image Stabilization
Directory of Open Access Journals (Sweden)
Nikolaos Kyriakoulis
2008-01-01
Full Text Available A novel digital image stabilization technique is proposed in this paper. It is based on a fuzzy Kalman compensation of the global motion vector (GMV, which is estimated in the log-polar plane. The GMV is extracted using four local motion vectors (LMVs computed on respective subimages in the logpolar plane. The fuzzy Kalman system consists of a fuzzy system with the Kalman filter's discrete time-invariant definition. Due to this inherited recursiveness, the output results into smoothed image sequences. The proposed stabilization system aims to compensate any oscillations of the frame absolute positions, based on the motion estimation in the log-polar domain, filtered by the fuzzy Kalman system, and thus the advantages of both the fuzzy Kalman system and the log-polar transformation are exploited. The described technique produces optimal results in terms of the output quality and the level of compensation.
Comparative Analysis of Fuzzy Inference Systems for Air Conditioner
Directory of Open Access Journals (Sweden)
Swati R. Chaudhari
2014-12-01
Full Text Available In today’s world there is exponential increase in the use of air conditioning devices. The enhancement in utilization of such devices makes it essential for them to work with their full capability and efficiency. The fuzzy inference systems are best suited for the applications requiring easy interpretation, human reasoning, accurate decision making and control. The fuzzy inference systems resemble human decision making and generate precise solutions from approximate information. A comprehensive review of fuzzy inference systems with weighted average and defuzzification is covered in this paper. The objective of the paper is to provide the comparative analysis of fuzzy inference systems. This paper is a quick reference for the researchers in studying the characteristics of fuzzy inference system in air conditioner.
Zischg, A.; Fuchs, S; M. Keiler; Meißl, G.
2005-01-01
International audience; The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and li...
Fuzzy diagnostic system for oleo-pneumatic drive mechanism of high-voltage circuit breakers.
Nicolau, Viorel
2013-01-01
Many oil-based high-voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo-pneumatic mechanism (MOP), presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP-type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short-term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies.
Fuzzy Diagnostic System for Oleo-Pneumatic Drive Mechanism of High-Voltage Circuit Breakers
Directory of Open Access Journals (Sweden)
Viorel Nicolau
2013-01-01
Full Text Available Many oil-based high-voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo-pneumatic mechanism (MOP, presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP-type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short-term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies.
Model Reduction of Fuzzy Logic Systems
Directory of Open Access Journals (Sweden)
Zhandong Yu
2014-01-01
Full Text Available This paper deals with the problem of ℒ2-ℒ∞ model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with an ℒ2-ℒ∞ error performance level γ but also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.
Expert finder systems – design and use
DEFF Research Database (Denmark)
Lykke, Marianne; Weidel, Eva
2011-01-01
expert finder systems to share knowledge about employees’ knowledge, interest, competences and activities. The purpose of the survey was to provide insight into goals, content and functionality of expert finder systems, including updating strategies and connection to social media knowledge sharing tools......, Switzerland, Ireland, United Kingdom, France, Portugal, Greece, Monaco, Italy, Luxemburg, Turkey, USA, Canada, Australia, New Zealand, Mexico, Chile, and China. The sample was drawn from Kompass: the business to business search engine, and covered service providers with 500+ employees and distributed location...... information, and behavioral data about activities, documents, network and preferences • Integration with social technologies is central - codification supports awareness and expertise retrieval, social networking supports sharing and interactive formation of knowledge...
Progress of Expert Systems in Electromagnetic Engineering
Institute of Scientific and Technical Information of China (English)
LAI Sheng-jian; WANG Bing-zhong
2005-01-01
It is urgent to solve various problems in electromagnetic (EM) engineering under the increasingly complicated environment. Some expert systems (ES) come into being just to keep up with the demand for solving these problems. Combined with the analysis of development of ES technology and the development trend of EM engineering software in recent years, the application of ES technology in EM engineering is discussed, and especially the progress of complete ES in electromagnetic compatible (EMC) is introduced.
Expert systems in the process industries
Stanley, G. M.
1992-01-01
This paper gives an overview of industrial applications of real-time knowledge based expert systems (KBES's) in the process industries. After a brief overview of the features of a KBES useful in process applications, the general roles of KBES's are covered. A particular focus is diagnostic applications, one of the major applications areas. Many applications are seen as an expansion of supervisory control. The lessons learned from numerous online applications are summarized.
Hypercube Expert System Shell - Applying Production Parallelism.
1989-12-01
that himian,, currentlly do better than machines (like the task of 3 piloting an aircraft ) lendl t wituselves to solution using an artificial ...research i ut eret tiit 1iw 1wrfuirtii i of artificial intelligence application software executing on a rriilt iconipilter. Liriti t liw analysis to...I IProc~ssing Compute- inteliive alilijcatiolis. such as the RAV expert system (28), require pro. cessing beyond the perforinance ability of
System control fuzzy neural sewage pumping stations using genetic algorithms
Directory of Open Access Journals (Sweden)
Владлен Николаевич Кузнецов
2015-06-01
Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.
Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers
Directory of Open Access Journals (Sweden)
Y. A. Al-Turki
2012-01-01
Full Text Available This paper presents a powerful supervisory power system stabilizer (PSS using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS. The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC driven by a fixed fuzzy set (FFS which has 49 rules. Both fuzzy logic controller (FLC algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.
Fuzzy Simulation Human Intelligent Control System Design on Gyratory Breaker
Institute of Scientific and Technical Information of China (English)
Wen,Ruchun; Zhao,Shuling; Zhu,Jianwu; Wang,Xiaoyan
2005-01-01
In order to deal with the complex process that incurs serious time delay, enormous inertia and nonlinear problems,fuzzy simulation human intelligent control algorithm rules are established. The fuzzy simulation human intelligent controller and the hardware with the single-chip microcomputer are designed and the anti-interference measures to the whole system are provided.
Induction machine Direct Torque Control system based on fuzzy adaptive control
Li, Shi-ping; Yu, Yan; Jiao, Zhen-gang; Gu, Shu-sheng
2009-07-01
Direct Torque Control technology is a high-performance communication control method, it uses the space voltage vector method, and then to the inverter switch state control, to obtain high torque dynamic performance. But none of the switching states is able to generate the exact voltage vector to produce the desired changes in torque and flux in most of the switching instances. This causes a high ripple in torque. To solve this problem, a fuzzy implementation of Direct Torque Control of Induction machine is presented here. Error of stator flux, error of motor electromagnetic torque and position of angle of flux are taken as fuzzy variables. In order to further solve nonlinear problem of variation parameters in direct torque control system, the paper proposes a fuzzy parameter PID adaptive control method which is suitable for the direct torque control of an asynchronous motor. The generation of its fuzzy control is obtained by analyzing and optimizing PID control step response and combining expert's experience. For this reason, it carries out fuzzy work to PID regulator of motor speed to achieve to regulate PID parameters. Therefore the control system gets swifter response velocity, stronger robustness and higher precision of velocity control. The computer simulated results verify the validity of this novel method.
A first course in fuzzy logic, fuzzy dynamical systems, and biomathematics theory and applications
de Barros, Laécio Carvalho; Lodwick, Weldon Alexander
2017-01-01
This book provides an essential introduction to the field of dynamical models. Starting from classical theories such as set theory and probability, it allows readers to draw near to the fuzzy case. On one hand, the book equips readers with a fundamental understanding of the theoretical underpinnings of fuzzy sets and fuzzy dynamical systems. On the other, it demonstrates how these theories are used to solve modeling problems in biomathematics, and presents existing derivatives and integrals applied to the context of fuzzy functions. Each of the major topics is accompanied by examples, worked-out exercises, and exercises to be completed. Moreover, many applications to real problems are presented. The book has been developed on the basis of the authors’ lectures to university students and is accordingly primarily intended as a textbook for both upper-level undergraduates and graduates in applied mathematics, statistics, and engineering. It also offers a valuable resource for practitioners such as mathematical...
Incomplete fuzzy data processing systems using artificial neural network
Patyra, Marek J.
1992-01-01
In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.
Expert systems for clinical pathology reporting.
Edwards, Glenn A
2008-08-01
* Conventional automated interpretative reporting systems use standard or "canned" comments for patient reports. These are result-specific and do not generally refer to the patient context. * Laboratory information systems (LIS) are limited in their application of patient-specific content of reporting. * Patient-specific interpretation requires extensive cross-referencing to other information contained in the LIS such as previous test results, other related tests, and clinical notes, both current and previous. * Expert systems have the potential to improve reporting quality by enabling patient-specific reporting in clinical laboratories.
Customization Using Fuzzy Recommender Systems
Institute of Scientific and Technical Information of China (English)
Ronald R. Yager
2004-01-01
We discuss some methods for constructing recommender systems. An important feature of the methods studied here is that we assume the availability of a description, representation, of the objects being considered for recommendation. The approaches studied here differ from collaborative filtering in that we only use pReferences information from the individual for whom we are providing the recommendation and make no use the preferences of other collaborators. We provide a detailed discussion of the construction of the representation schema used. We consider two sources of information about the users preferences. The first are direct statements about the type of objects the user likes. The second source of information comes from ratings of objects which the user has experienced.
A New Fuzzy System Based on Rectangular Pyramid
Jiang, Mingzuo; Yuan, Xuehai; Li, Hongxing; Wang, Jiaxia
2015-01-01
A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation. PMID:25874253
A kind of fuzzy control for chaotic systems
Institute of Scientific and Technical Information of China (English)
WANG Hong-wei; MA Guang-fu
2007-01-01
With a T-S fuzzy dynamic model approximating to a non-linear system, the nonlinear system can be decomposed into some local linear models. A variable structure controller based on Lyapunov theories is designed to guarantee the global stability of the T-S fuzzy model. The controlling problems of a nonlinear system can be solved by means of consisting of linear system variable structure control and fuzzy control. The validity of the control method based on the simulating result of two kinds of chaotic systems is shown here.
An Expert System for Concrete Bridge Management
DEFF Research Database (Denmark)
Brito, J. de; Branco, F. A.; Thoft-Christensen, Palle
1997-01-01
management systems are presently being implemented by bridge authorities in several countries. The prototype of an expert system for concrete bridge management is presented in this paper, with its functionality relying on two modules. The inspection module relies on a periodic acquisition of field......The importance of bridge repair versus new bridge construction has risen in recent decades due to high deterioration rates that have been observed in these structures. Budgets both for building new bridges and keeping the existing ones are always limited. To help rational decision-making, bridge...
A New Approach for Solving Fully Fuzzy Linear Systems
Directory of Open Access Journals (Sweden)
Amit Kumar
2011-01-01
Full Text Available Several authors have proposed different methods to find the solution of fully fuzzy linear systems (FFLSs that is, fuzzy linear system with fuzzy coefficients involving fuzzy variables. But all the existing methods are based on the assumption that all the fuzzy coefficients and the fuzzy variables are nonnegative fuzzy numbers. In this paper a new method is proposed to solve an FFLS with arbitrary coefficients and arbitrary solution vector, that is, there is no restriction on the elements that have been used in the FFLS. The primary objective of this paper is thus to introduce the concept and a computational method for solving FFLS with no non negative constraint on the parameters. The method incorporates the principles of linear programming in solving an FFLS with arbitrary coefficients and is not only easier to understand but also widens the scope of fuzzy linear equations in scientific applications. To show the advantages of the proposed method over existing methods we solve three FFLSs.
Modeling and control of an unstable system using probabilistic fuzzy inference system
Directory of Open Access Journals (Sweden)
Sozhamadevi N.
2015-09-01
Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.
Supplier Selection Using Fuzzy Inference System
Directory of Open Access Journals (Sweden)
hamidreza kadhodazadeh
2014-01-01
Full Text Available Suppliers are one of the most vital parts of supply chain whose operation has significant indirect effect on customer satisfaction. Since customer's expectations from organization are different, organizations should consider different standards, respectively. There are many researches in this field using different standards and methods in recent years. The purpose of this study is to propose an approach for choosing a supplier in a food manufacturing company considering cost, quality, service, type of relationship and structure standards of the supplier organization. To evaluate supplier according to the above standards, the fuzzy inference system has been used. Input data of this system includes supplier's score in any standard that is achieved by AHP approach and the output is final score of each supplier. Finally, a supplier has been selected that although is not the best in price and quality, has achieved good score in all of the standards.
An expert system for ranking companies and investments: wood industry case
Directory of Open Access Journals (Sweden)
Hliadis Lazaros
2003-01-01
Full Text Available Wood industry is a very important part of both the Greek Rural and industrial sector. The discovery of the differentiation in the level of growth and in the quality of financial management between the Greek wood companies can provide very important aid in the design of an effective rural development policy. The evaluation and ranking of Greek wood companies based on actual financial data is a very complicated task and it requires expertise knowledge and skills. On the other hand a computer expert system can perform validation and evaluation in an efficient way and can substitute human experts. An expert system was designed and developed towards this direction. It uses multicriteria analysis for each one of the wood companies based on actual financial data and it applies fundamental principles of fuzzy logic in order to calculate the expected intervals of flows for the following years. .
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.
System control module diagnostic Expert Assistant
Flores, Luis M.; Hansen, Roger F.
1990-01-01
The Orbiter EXperiments (OEX) Program was established by NASA's Office of Aeronautics and Space Technology (OAST) to accomplish the precise data collection necessary to support a complete and accurate assessment of Space Transportation System (STS) Orbiter performance during all phases of a mission. During a mission, data generated by the various experiments are conveyed to the OEX System Control Module (SCM) which arranges for and monitors storage of the data on the OEX tape recorder. The SCM Diagnostic Expert Assistant (DEA) is an expert system which provides on demand advice to technicians performing repairs of a malfunctioning SCM. The DEA is a self-contained, data-driven knowledge-based system written in the 'C' Language Production System (CLIPS) for a portable micro-computer of the IBM PC/XT class. The DEA reasons about SCM hardware faults at multiple levels; the most detailed layer of encoded knowledge of the SCM is a representation of individual components and layouts of the custom-designed component boards.
An expert system for selecting wart treatment method.
Khozeimeh, Fahime; Alizadehsani, Roohallah; Roshanzamir, Mohamad; Khosravi, Abbas; Layegh, Pouran; Nahavandi, Saeid
2017-02-01
As benign tumors, warts are made through the mediation of Human Papillomavirus (HPV) and may grow on all parts of body, especially hands and feet. There are several treatment methods for this illness. However, none of them can heal all patients. Consequently, physicians are looking for more effective and customized treatments for each patient. They are endeavoring to discover which treatments have better impacts on a particular patient. The aim of this study is to identify the appropriate treatment for two common types of warts (plantar and common) and to predict the responses of two of the best methods (immunotherapy and cryotherapy) to the treatment. As an original work, the study was conducted on 180 patients, with plantar and common warts, who had referred to the dermatology clinic of Ghaem Hospital, Mashhad, Iran. In this study, 90 patients were treated by cryotherapy method with liquid nitrogen and 90 patients with immunotherapy method. The selection of the treatment method was made randomly. A fuzzy logic rule-based system was proposed and implemented to predict the responses to the treatment method. It was observed that the prediction accuracy of immunotherapy and cryotherapy methods was 83.33% and 80.7%, respectively. According to the results obtained, the benefits of this expert system are multifold: assisting physicians in selecting the best treatment method, saving time for patients, reducing the treatment cost, and improving the quality of treatment. Copyright © 2017 Elsevier Ltd. All rights reserved.
Expert System for Diagnosis of Hepatitis B Ibrahim Mailafiya, Fatima ...
African Journals Online (AJOL)
Department of Computer Science,. Nasarawa State University, Keffi. Abstract. This paper is a preview of the work so far concluded on Expert Systems ... computing research and the real application ... A web-based expert system for wheat.
Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications
Directory of Open Access Journals (Sweden)
Radu-Emil Precup
2006-01-01
Full Text Available The development of fuzzy control systems is usually performed by heuristicmeans, incorporating human skills, the drawback being in the lack of general-purposedevelopment methods. A major problem, which follows from this development, is theanalysis of the structural properties of the control system, such as stability, controllabilityand robustness. Here comes the first goal of the paper, to present a stability analysismethod dedicated to fuzzy control systems with mechatronics applications based on the useof Popov’s hyperstability theory. The second goal of this paper is to perform the sensitivityanalysis of fuzzy control systems with respect to the parametric variations of the controlledplant for a class of servo-systems used in mechatronics applications based on theconstruction of sensitivity models. The stability and sensitivity analysis methods provideuseful information to the development of fuzzy control systems. The case studies concerningfuzzy controlled servo-systems, accompanied by digital simulation results and real-timeexperimental results, validate the presented methods.
Secondary systems modeled as fuzzy sub-structures
DEFF Research Database (Denmark)
Tarp-Johansen, Niels Jacob; Ditlevsen, Ove Dalager; Lin, Y.K.
1998-01-01
in the simplest case be modeled by attaching random single degree of freedom oscillators, called fuzzies, to the master structure at randomly distributed points of the structure. Each of these fuzzies are characterized by a random triplet of mass, eigenfrequency, and damping ratio. This characterization can...... be combined with a model of the random distribution of the fuzzies over the structure by letting the entire system of fuzzies be characterized as a triplet of random fields over the structure. Two specific examples, a Poisson point pulse field and a Poisson square wave field, of such a triplet field...... the probabilistic properties of the impulse response function, say, or of the nonergodic steady state response to stationary excitation, say. The study prepares for a finite element model of a flexible master structure with a fuzzy subsystem attached to it....
Rotating Machinery Predictive Maintenance Through Expert System
Directory of Open Access Journals (Sweden)
M. Sarath Kumar
2000-01-01
Full Text Available Modern rotating machines such as turbomachines, either produce or absorb huge amount of power. Some of the common applications are: steam turbine-generator and gas turbine-compressor-generator trains produce power and machines, such as pumps, centrifugal compressors, motors, generators, machine tool spindles, etc., are being used in industrial applications. Condition-based maintenance of rotating machinery is a common practice where the machine's condition is monitored constantly, so that timely maintenance can be done. Since modern machines are complex and the amount of data to be interpreted is huge, we need precise and fast methods in order to arrive at the best recommendations to prevent catastrophic failure and to prolong the life of the equipment. In the present work using vibration characteristics of a rotor-bearing system, the condition of a rotating machinery (electrical rotor is predicted using an off-line expert system. The analysis of the problem is carried out in an Object Oriented Programming (OOP framework using the finite element method. The expert system which is also developed in an OOP paradigm gives the type of the malfunctions, suggestions and recommendations. The system is implemented in C++.
Network Structure Expert System and Operation Optimization
Institute of Scientific and Technical Information of China (English)
刘洪谦; 袁希钢; 麻德贤
2003-01-01
It is proposed that double level programming technique may be adopted in synthesis strategy. Optimization of heat exchanger network structural configuration (the master problem) may be solved at the upper level, leaving the rest operating conditions( the slave problem) being optimized at the lower level. With the uniqueness in mind, an HEN synthesis expert system may be employed to address both the logical constraints and the global operation parameters′ optimization using enhanced sequential number optimization theory.Case studies demonstrate that the synthesis strategy proposed can effectively simplify both the problem-solving and the synthesis process. The validity of the strategy recommended is evidenced by case studies′ results compared.
WES: A well test analysis expert system
Energy Technology Data Exchange (ETDEWEB)
Mensch, A.
1988-06-01
This report describes part of the development of an expert system in the domain of well-test analysis. This work has been done during my final internship, completed at the Lawrence Berkeley Laboratory. The report is divided in three parts: the first one gives a description of the state of the project at the time I first began to work on it, and raises some problems that have to be solved. The second section shows the results that have been reached, and the last one draws conclusions from these results and proposes extensions that would be useful in the future.
Fuzzy modelling and impulsive control of the hyperchaotic Lü system
Institute of Scientific and Technical Information of China (English)
Zhang Xiao-Hong; Li Dong
2009-01-01
This paper presents a novel approach to hyperchvos control of hyperchaotic systems based on impulsive control and the Takagi-Sugeno (T-S) fuzzy model. In this study, the hyperchaotic Lü system is exactly represented by the T-S fuzzy model and an impulsive control framework is proposed for stabilizing the hyperchaotic Lü system, which is also suitable for classes of T-S fuzzy hyperchaotic systems, such as the hyperchaotic Rossler, Chen, Chua systems and so on. Sufficient conditions for achieving stability in impulsive T-S fuzzy hyperchaotic systems are derived by using Lyapunov stability theory in the form of the linear matrix inequality, and axe less conservative in comparison with existing results. Numerical simulations are given to demonstrate the effectiveness of the proposed method.
Expert Graphics System Research in the Department of the Navy.
Duff, Jon M.
1987-01-01
Presents current trends in the development of expert systems within the Department of the Navy, particularly research into expert graphics systems intended to support the Authoring Instructional Methods (AIM) research project. Defines artificial intelligence and expert systems. Discusses the operations and functions of the Navy's intelligent…
Expert System for Software Quality Assurance. User’s Manual,
1986-11-01
This user’s manual describes the execution of an expert system for Software Quality Assurance (SQA). The objective of the expert system is to capture...which are all included in the batch file called SQA.BAT. The primary component is the expert system , which was developed using the EXSYS development
Expert System Shells: Tools to Aid Human Performance.
Welsh, Jack R.; Wilson, Brent G.
1987-01-01
Examines expert system shells and the role a microcomputer-based expert system can play as an intelligent job aid. Characteristics of traditional and automated job aids techniques are described, and the role of instructional designers in developing expert systems within organizations is discussed. (Author/LRW)
Expert Systems--The New International Language of Business.
Sondak, Norman E.; And Others
A discussion of expert systems, computer programs designed to simulate human reasoning and expertise, begins with the assumption that few business educators understand the impact that expert systems will have on international business. The fundamental principles of the design and development of expert systems in business are outlined, with special…
Applied intelligent systems: blending fuzzy logic with conventional control
Filev, Dimitar; Syed, Fazal U.
2010-05-01
The aim of this paper is to show that design of applied intelligent control systems requires different types of blending between fuzzy logic and conventional control systems. Two alternative automotive applications - a manufacturing process control problem and an advisory system for fuel efficient driving - that benefit from both fuzzy and control theories are reviewed and different levels of prioritisations of both approaches are discussed based on the specificity of the applications.
FUZZY CONTROLLED AUTOMATION SYSTEM FOR THE MAIN COAL BUNKER
Institute of Scientific and Technical Information of China (English)
邵良杉; 叶景楼; 付华
1997-01-01
A fuzzy control scheme is presented according to the coal quantity in the main coal bunker, this method has a good dynamic response characteristic and is suited for complex nonlinear systems. The designation of self-adopting fuzzy controller, the working principle and functions of this system are also proposed, with the hardware and the main flow diagram of this system introduced in this paper.
Hybrid Fuzzy Sliding Mode Controller for Timedelay System
Yadav, N K; R. K. Singh,
2013-01-01
This paper is concerned with the problems of stability analysis and stabilization control design for a class of discrete-time T-S fuzzy systems with state-delay for multi-input and multi-output. The nonlinear fuzzy controller helps to overcome the problems of the ill - defined model of the systems, which are creating the undesirable performance. . Here sliding surface is being designed for error function of nonlinear system and sliding mode control is being designed here. The swit...
Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications
Radu-Emil Precup; Stefan Preitl
2006-01-01
The development of fuzzy control systems is usually performed by heuristicmeans, incorporating human skills, the drawback being in the lack of general-purposedevelopment methods. A major problem, which follows from this development, is theanalysis of the structural properties of the control system, such as stability, controllabilityand robustness. Here comes the first goal of the paper, to present a stability analysismethod dedicated to fuzzy control systems with mechatronics applications bas...
Techniques for capturing expert knowledge - An expert systems/hypertext approach
Lafferty, Larry; Taylor, Greg; Schumann, Robin; Evans, Randy; Koller, Albert M., Jr.
1990-01-01
The knowledge-acquisition strategy developed for the Explosive Hazards Classification (EHC) Expert System is described in which expert systems and hypertext are combined, and broad applications are proposed. The EHC expert system is based on rapid prototyping in which primary knowledge acquisition from experts is not emphasized; the explosive hazards technical bulletin, technical guidance, and minimal interviewing are used to develop the knowledge-based system. Hypertext is used to capture the technical information with respect to four issues including procedural, materials, test, and classification issues. The hypertext display allows the integration of multiple knowlege representations such as clarifications or opinions, and thereby allows the performance of a broad range of tasks on a single machine. Among other recommendations, it is suggested that the integration of hypertext and expert systems makes the resulting synergistic system highly efficient.
Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers
Directory of Open Access Journals (Sweden)
N. V. Kolesov
2013-01-01
Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.
Generalized multidirectional fuzzy map model of the logistics system networks
Ji, Chun-Rong; Liu, Ming-Yuan; Li, Yan; He, Yue M.
1997-07-01
By conducting [0, 1] treatment to time consuming of logistics system network key links, and regarding the time consumed by manufacture, inspection, storage, assembling, packing and market as a kind of existent extent of the joint and the time consumed by materials handling, transportation and logistics information as the connection strength between joints in a generalized multi-directional fuzzy map, a generalized multi-directional fuzzy map model of logistics system networks is built. The mutual flow among network joints and the special form of generalized fuzzy matrix is analyzed. Finally, an example of model building is given.
FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM
Institute of Scientific and Technical Information of China (English)
Luo Xiaobin; Yin Guofu; Chen Ke; Hu Xiaobing; Luo Yang
2003-01-01
The primary purpose is to develop a robust adaptive machine parts recognition system. A fuzzy neural network classifier is proposed for machine parts classifier. It is an efficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzy neural network classifier is presented based on fuzzy mapping model. It is used for machine parts classification. The experimental system of machine parts classification is introduced. A robust least square back-propagation (RLSBP) training algorithm which combines robust least square (RLS) with back-propagation (BP) algorithm is put forward. Simulation and experimental results show that the learning property of RLSBP is superior to BP.
FUZZY MAPPING IN DATA SONIFICATION SYSTEM OF WIRELESS SENSOR NETWORK
Directory of Open Access Journals (Sweden)
Arseny A. Markhotin
2016-11-01
Full Text Available Problem Statement. This paper describes the modeling of sonification system with possible types of wireless sensor network data. Fuzzy logic is used for the data-to-sound mapping. Methods. Devised sonification system includes input data model and sound synthesis core. It was created in Pure Data. For fuzzy output of mapped data the Fuzzy Logic Toolboxof MATLABwas used. Moreover, the system model has an ability to send data to the side application via UDP protocol. Results. We offer the method of timbre space organization for sonification system output and the following output of control sound characteristics depending on the type of input data. Practical Relevance. The offered approach of using fuzzy logic in sonification systems can be applied in development of new applications when the formalization of data-to-sound mapping is difficult and also complicated timbal space organization is required.
Genetic fuzzy system modeling and simulation of vascular behaviour
DEFF Research Database (Denmark)
Tang, Jiaowei; Boonen, Harrie C.M.
in cardiovascular disease and ultimately improve pharmacotherapy. For this purpose, novel computational approaches incorporating adaptive properties, auto-regulatory control and rule sets will be assessed, properties that are commonly lacking in deterministic models based on differential equations. We hypothesize...... in principle for any physiological system that is characterized by auto-regulatory control and adaptation. Methods: Currently, one modeling approach is being investigated, Genetic Fuzzy System (GFS). In Genetic Fuzzy Systems, the model algorithm mimics the biologic genetic evolutionary process to learn...... chromosome or individual to define the fuzzy system. The model is implemented by combining the Matlab Genetic algorithm and Fuzzy system toolboxes, respectively. To test the performance of this method, experimental data sets about calculated pressure change in different blood vessels after several chemical...
Fuzzy-AHP-Based Comprehensive Evaluation on Facility Management System of High-Rise Office Buildings
Institute of Scientific and Technical Information of China (English)
ZHANG Peihong; WANG Kan; WAN Huanhuan; MA Zhongjiao
2011-01-01
The present building facility management status in China resulted in many problems such as highenergy consumption, failure of automation control, services failure and poor indoor air quality. Based onquestionnaires and interviews to professional engineers and building users, a comprehensive evaluation index system was established on facility management of high-rise office buildings. A Fuzzy AHP based upon hierarchy criteria system was established. A Fuzzy AHP Evaluation Model on Facility Management System was set up ;α-cut analysis was introduced and incorporated with expert knowledge together, which made up the optimism index λ. The fuzzy optimum crisp weight of each criterion was resulted from data-mining. Case investigations were processed in high-rise office buildings in Shenyang. The results illustrated that indoor air quality, thermal comfort and life cycle cost were the most important indexes in the evaluation of Facility Management System of high rise office buildings. Residents in high-rise buildings in Shenyang pay less attention to maintenance management and environment protection. By comparison with the analysis result of Export Choice, Fuzzy AHP-based evaluation model could act as a scientific reference for the establishment of governmental standards in facility management area in building.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Variable universe stable adaptive fuzzy control of nonlinear system
Institute of Scientific and Technical Information of China (English)
李洪兴; 苗志宏; 王加银
2002-01-01
A kind of stable adaptive fuzzy control of nonlinear system is implemented using variable universe method. First of all, the basic structure of variable universe adaptive fuzzy controllers is briefly introduced. Then the contraction-expansion factor that is a key tool of variable universe method is defined by means of integral regulation idea, and a kind of adaptive fuzzy controllers is designed by using such a contraction-expansion factor. The simulation on first order nonlinear system is done. Secondly, it is proved that the variable universe adaptive fuzzy control is asymptotically stable by use of Lyapunov theory. The simulation on the second order nonlinear system shows that its simulation effect is also quite good. Finally a useful tool, called symbolic factor, is proposed, which may be of universal significance. It can greatly reduce the settling time and enhance the robustness of the system.
Fuzzy rank functions in the set of all binary systems.
Kim, Hee Sik; Neggers, J; So, Keum Sook
2016-01-01
In this paper, we introduce fuzzy rank functions for groupoids, and we investigate their roles in the semigroup of binary systems by using the notions of right parallelisms and [Formula: see text]-shrinking groupoids.
CENTRIC MANAGEMENT SYSTEM BASED ON NEURO - FUZZY TOPOLOGY
Directory of Open Access Journals (Sweden)
Shumkov Y. A.
2014-11-01
Full Text Available The article describes the network-centric approach to a building control system based on the "inner teacher" neuro - fuzzy topology, which uses the principles of reinforcement learning
Genetic fuzzy system predicting contractile reactivity patterns of small arteries
DEFF Research Database (Denmark)
Tang, J; Sheykhzade, Majid; Clausen, B F;
2014-01-01
strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used...
Robust support vector machine-trained fuzzy system.
Forghani, Yahya; Yazdi, Hadi Sadoghi
2014-02-01
Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.
CASCADED FUNZZY SYSTEM AND ITS ROBUST ANALYSIS BASED ON SYLLOGISTIC FUZZY REASONING
Institute of Scientific and Technical Information of China (English)
Wang Shitong; Korris F. L. Chung
2004-01-01
Syllogistic fuzzy reasoning is introduced into fizzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is more robust than all other implication inferences for noise data and that CFS has better robustness than conventional fuzzy systems, which provide the solid foundation for CFS's potential application in fuzzy control and modeling and so on.
Design of an expert system to automatically calibrate impedance control for powered knee prostheses.
Wang, Ding; Liu, Ming; Zhang, Fan; Huang, He
2013-06-01
Many currently available powered knee prostheses (PKP) use finite state impedance control to operate a prosthetic knee joint. The desired impedance values were usually manually calibrated with trial-and-error in order to enable near-normal walking pattern. However, such a manual approach is inaccurate, time consuming, and impractical. This paper aimed to design an expert system that can tune the control impedance for powered knee prostheses automatically and quickly. The expert system was designed based on fuzzy logic inference (FLI) to match the desired knee motion and gait timing while walking. The developed system was validated on an able-bodied subject wearing a powered prosthesis. Preliminary experimental results demonstrated that the developed expert system can converge the user's knee profile and gait timing to the desired values within 2 minutes. Additionally, after the auto-tuning procedure, the user produced more symmetrical gait. These preliminary results indicate the promise of the designed expert system for quick and accuracy impedance calibration, which can significantly improve the practical value of powered lower limb prosthesis. Continuous engineering efforts are still needed to determine the calibration objectives and validate the expert system.
MODELLING OF AIR CONDITIONING SYSTEM BY FUZZY LOGIC APPROACH
Directory of Open Access Journals (Sweden)
Ahmet ÖZEK
2004-03-01
Full Text Available One of the main problems in control systems is the difficulty to form the mathematical model associated with the control mechanism. Even though this model can be formed, to realize the application with conventional logic may cause very complex problems. The fuzzy logic without using mathematical model of control system can create control mechanism only with the help of linguistic variables. In this article the modeling has been realized by fuzzy logic.
Directory of Open Access Journals (Sweden)
Mohit Jha,
2014-06-01
Full Text Available During the past several years fuzzy logic control has swell from one of the major active and profitable areas for research in the application of fuzzy set, especially in the zone of industrial process which do not lead themselves to control conventional methods because of lack of quantitative data regarding the input-output relations. Fuzzy control is based on fuzzy logic- a logical system which is much closer in spirit to human thinking and natural language than conventional logical systems. The fuzzy logic controller based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. As in Fuzzy logic traffic controller, the need arises for simulating and optimizing traffic control algorithms to better accommodate this increasing demand. Fuzzy optimization deals with finding the values of input parameters of a complex simulated system which result in desired output. This paper presents a MATLAB simulation of fuzzy logic traffic interval type II controller for controlling flow of traffic in multilane paths. This controller is based on the waiting time and queue length of vehicles at present green phase and vehicles queue lengths at the other lanes. The controller controls the traffic light timings and phase difference to ascertain sebaceous flow of traffic with least waiting time and queue length. In this paper, the multilane model used consists of two alleyways in each approach.
Enhanced Decision Support Systems in Intensive Care Unit Based on Intuitionistic Fuzzy Sets
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Hanen Jemal
2017-01-01
Full Text Available In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU. Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS, the shown work has its origins in the Modified Early Warning Score (MEWS standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove that the MIFEDSS does actually display an imposing capability for an established handily ICU related uncertainty issues. The performance of the prototypes is compared with the MEWS standard which exposed that the IFS application appears to perform highly better in deferring accuracy than the expert MEWS score with higher degrees of sensitivity and specificity being recorded.
Marginal linearization method in modeling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Marginal linearization method in modeling on fuzzy control systems is proposed, which is to deal with the nonlinear model with variable coefficients. The method can turn a nonlinear model with variable coefficients into a linear model with variable coefficients in the way that the membership functions of the fuzzy sets in fuzzy partitions of the universes are changed from triangle waves into rectangle waves. However, the linearization models are incomplete in their forms because of their lacking some items. For solving this problem, joint approximation by using linear models is introduced. The simulation results show that marginal linearization models are of higher approximation precision than their original nonlinear models.
Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System
Directory of Open Access Journals (Sweden)
H. Q. Hou
2014-06-01
Full Text Available Sliding mode controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with; however it is practically impossible to achieve high-speed switching control. Therefore, in this paper an adaptive fuzzy backstepping sliding mode control scheme is derived for mismatched uncertain systems. Firstly fuzzy sliding mode controller is designed using backstepping method based on the Lyapunov function approach, which is capable of handling mismatched problem. Then fuzzy sliding mode controller is designed using T-S fuzzy model method, it can improve the performance of the control systems and their robustness. Finally this method of control is applied to nonlinear system as a case study; simulation results are also provided the performance of the proposed controller.
Robust adaptive fuzzy control scheme for nonlinear system with uncertainty
Institute of Scientific and Technical Information of China (English)
Mingjun ZHANG; Huaguang ZHANG
2006-01-01
In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.
Fuzzy Variable Structure Control of Photovoltaic MPPT System
Institute of Scientific and Technical Information of China (English)
LI Wei; ZHU Xin-jian; CAO Guang-yi
2006-01-01
In order to reduce chattering phenomenon of variable structure control, a fuzzy variable structure control method is adopted and applied in the photovoitaic maximum power point tracking (MPPT) control system. Firstly, the electric features of PV cells and a dynamic model of photovoltaic system with a DC-DC buck converter are analysed. Then a hybrid fuzzy variable structure controller is designed. The controller is composed of a fuzzy variable structure control term and a supervisory control term. The former is the main part of the controller and the latter is used to ensure the stability of the system. Finally, the conventional variable structure control method and the fuzzy variable structure control method are applied respectively. The comparing of simulation results shows the superiority of the latter.
An Automatic KANSEI Fuzzy Rule Creating System Using Thesaurus
Hotta, Hajime; Hagiwara, Masafumi
In this paper, we propose an automatic Kansei fuzzy rule creating system using thesaurus. In general, there are a lot of words that express impressions. However, conventional approaches of Kansei engineering are not suitable to use many impression words because it is difficult to collect enough data. The proposed system is an enhanced algorithm of the conventional method that the authors proposed before. The proposed system extracts fuzzy rules for many words defined in the thesaurus dictionary while the conventional one can extract rules of specified words which user defined. The flow of the system consists of 3 steps: (1) construction of thesaurus networks; (2) data collection by web questionnaire sheets; (3) Extraction of fuzzy rules. In order to extract Kansei fuzzy rules, the system employs enhanced GRNN(general regression neural network) which can treat relative words of the thesaurus network. Using a Japanese thesaurus dictionary in the experiments, the sets of fuzzy rules for 1,195 impression words are extracted, and the fuzzy rules extracted by the proposed system obtained higher accuracy than those extracted by the conventional one.
A Belief Rule Based Expert System to Assess Mental Disorder under Uncertainty
DEFF Research Database (Denmark)
Hossain, Mohammad Shahadat; Afif Monrat, Ahmed; Hasan, Mamun;
2016-01-01
Mental disorder is a change of mental or behavioral pattern that causes sufferings and impairs the ability to function in ordinary life. In psychopathology, the assessment methods of mental disorder contain various types of uncertainties associated with signs and symptoms. This study identifies...... a method that addresses the issue of uncertainty in assessing mental disorder. The fuzzy logic knowledge representation schema can address uncertainty associated with linguistic terms including ambiguity, imprecision, and vagueness. However, fuzzy logic is incapable of addressing uncertainty due...... to ignorance, incompleteness, and randomness. So, a belief rule-based expert system (BRBES) has been designed and developed with the capability of handling the uncertainties mentioned. Evidential reasoning works as the inference engine and the belief rule base as the knowledge representation schema...
The Application of Big-Neuron Theory in Expert Systems
Institute of Scientific and Technical Information of China (English)
李涛
2001-01-01
With a new way of knowledge representation and acquirement, inference, and building an expert system based on big-neurons composed of different field expert knowledge presented, the fundamental theory and architecture of expert system based upon big-neuron theory has thus been built. It is unnecessary to organize a large number of production rules when using big-neurons to build an expert system. The facts and rules of an expert system have already been hidden in big-neurons. And also, it is unnecessary to do a great quantity of tree searching when using this method to do logic reasoning. Machine can do self-organizing and self-learning.
Uncertain rule-based fuzzy systems introduction and new directions
Mendel, Jerry M
2017-01-01
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy se...
Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems
Roopaei, Mehdi; Zolghadri, Mansoor; Meshksar, Sina
2009-09-01
In this article, a novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC). Making use of the SMC design framework, we propose two fuzzy systems to be used as reaching and equivalent parts of the SMC. In this way, we make use of the fuzzy logic to handle uncertainty/disturbance in the design of the equivalent part and provide a chattering free control for the design of the reaching part. To construct the equivalent control law, an adaptive fuzzy inference engine is used to approximate the unknown parts of the system. To get rid of the chattering, a fuzzy logic model is assigned for reaching control law, which acting like the saturation function technique. The main advantage of our proposed methodology is that the structure of the system is unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are required in advance. Using Lyapunov stability theory and Barbalat's lemma, the closed-loop system is proved to be stable and convergence properties of the system is assured. Simulation examples are presented to verify the effectiveness of the method. Results are compared with some other methods proposed in the past research.
Advances in type-2 fuzzy sets and systems theory and applications
Mendel, Jerry; Tahayori, Hooman
2013-01-01
This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet. The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.
Fuzzy rule-based support vector regression system
Institute of Scientific and Technical Information of China (English)
Ling WANG; Zhichun MU; Hui GUO
2005-01-01
In this paper,we design a fuzzy rule-based support vector regression system.The proposed system utilizes the advantages of fuzzy model and support vector regression to extract support vectors to generate fuzzy if-then rules from the training data set.Based on the first-order linear Tagaki-Sugeno (TS) model,the structure of rules is identified by the support vector regression and then the consequent parameters of rules are tuned by the global least squares method.Our model is applied to the real world regression task.The simulation results gives promising performances in terms of a set of fuzzy rules,which can be easily interpreted by humans.
Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System
Directory of Open Access Journals (Sweden)
Stephan Birle
2016-01-01
Full Text Available In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the
Identification of uncertain nonlinear systems for robust fuzzy control.
Senthilkumar, D; Mahanta, Chitralekha
2010-01-01
In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by Skrjanc et al. (2005). With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.
GA-Based Fuzzy Sliding Mode Controller for Nonlinear Systems
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W. L. Chiang
2008-11-01
Full Text Available Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC or an adaptive fuzzy sliding mode controller (AFSMC capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA-based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.
Shukla, Shailja
2010-01-01
... This paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic [8] [9] approach for steering and speed control [37], a FL approach for ultrasound sensing and an overall expert system for guidance. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test bed has been constructed in university of Cincinnati using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised through a multi-axis motion controller. The obstacle avoidance system is based on a microcontroller interfaced with ultrasonic transducers. This micro-controller independently handles all timing and distance calculations and sends distance information back to the fuzzy logic controller via the serial ...
Fuzzy logic controllers: A knowledge-based system perspective
Bonissone, Piero P.
1993-01-01
Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.
Simulation Study of IMC and Fuzzy Controller for HVAC System
Directory of Open Access Journals (Sweden)
Umamaheshwari
2009-06-01
Full Text Available This paper presents how the fuzzy logic controller is used to solve the control problems of complex and non linear process and show that it is more robust and their performance are less sensitive to parametric variations than conventional controllers. These systems will yield a linear response when compared to ordinary controllers. The main advantage of Fuzzy control over conventional controllers is regulation can be done without over shoot.
Stabilizing Planar Inverted Pendulum System Based on Fuzzy Nine-point Controller
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Qi Qian
2013-07-01
Full Text Available In order to stabilize planar inverted pendulum, after analyzing the physical characteristics of the planar inverted pendulum system, a pendulum nine-point controller and a car nine-point controller for X-axis and Y-axis were designed respectively. Then a fuzzy coordinator was designed using the fuzzy control theory based on the priority of those two controllers, and the priority level of the pendulum is higher than the car. Thus, the control tasks of each controller in each axis were harmonized successfully. The designed control strategy did not depend on mathematical model of the system, it depended on the control experience of people or the control experts. The compared experiments showed that the control strategy was easy and effective, what was’s more, it had a very good robust feature.
Expert system to control a fusion energy experiment
Energy Technology Data Exchange (ETDEWEB)
Johnson, R.R.; Canales, T.; Lager, D.
1986-01-01
This paper describes a system that automates neutral beam source conditioning. The system achieves this with artificial intelligence techniques by encoding the behavior of several experts as a set of if-then rules in an expert system. One of the functions of the expert system is to control an adaptive controller that, in turn, controls the neutral beam source. The architecture of the system is presented followed by a description of its performance.
Vedic Mathematics: 'Vedic' or 'Mathematics' -- A Fuzzy and Neutrosophic Analysis
2006-01-01
In this book the authors probe into Vedic Mathematics (a concept that gained renown in the period of the religious fanatic and revivalist Hindutva rule in India): and explore whether it is really 'Vedic' in origin or 'Mathematics' in content. We analyzed this problem using fuzzy models like Fuzzy Cognitive Maps (FCM), Fuzzy Relational Maps (FRM) and the newly constructed fuzzy dynamical system (and its Neutrosophic analogue) that can analyze multi-experts opinion at a time using a single mode...
Developing of an expert system for nonferrous alloy design
Institute of Scientific and Technical Information of China (English)
李义兵; 何红波; 周继承; 李斌
2004-01-01
Expert systems have been used widely in the predictions and design of alloy systems. But the expert systems are based on the macroscopic models that have no physical meanings. Microscopic molecular dynamics is also a standard computational technique used in materials science. An approach is presented to the design system of nonferrous alloy that integrates the molecular dynamical simulation together with an expert system. The knowledge base in the expert system is able to predict nonferrous alloy properties by using machine learning technology. The architecture of the system is presented.
Expert System for Data Security Risk Management for SMEs
Directory of Open Access Journals (Sweden)
Justinas Janulevičius
2013-05-01
Full Text Available Accessibility of expertise and expert inferences is one of the key factors for appropriate expert evaluation. Appropriate and timely expert information allows a smooth process of expertise. Small and medium enterprises (SMEs have limited possibilities to acquire professional expertise for data security risk analysis due to limited finances. A risk management expert system is developed for SMEs with the ability to adapt to various subject domains using ontologies of the field.Article in Lithuanian
Expert System For Diagnosis Of Skin Diseases
Directory of Open Access Journals (Sweden)
A.A.L.C. Amarathunga
2015-01-01
Full Text Available Abstract Dermatology is a one of major session of medicine that concerned with the diagnosis and treatment of skin diseases. Skin diseases are the most common form of disease in humans. Recently many of researchers have advocated and developed the imaging of human vision or in the loop approach to visual object recognition. This research paper presents a development of a skin diseases diagnosis system which allows user to identify diseases of the human skin and to provide advises or medical treatments in a very short time period. For this purpose user will have to upload an image of skin disease to our system and answer questions based on their skin condition or symptoms. It will be used to detect diseases of the skin and offer a treatment recommendation. This system uses technologies such as image processing and data mining for the diagnosis of the disease of the skin. The image of skin disease is taken and it must be subjected to various preprocessing for noise eliminating and enhancement of the image. This image is immediately segmentation of images using threshold values. Finally data mining techniques are used to identify the skin disease and to suggest medical treatments or advice for users. This expert system exhibits disease identification accuracy of 85 for Eczema 95 for Impetigo and 85 for Melanoma.
2014-01-01
The purpose of this paper is to create an interval estimation of the fuzzy system reliability for the repairable multistate series–parallel system (RMSS). Two-sided fuzzy confidence interval for the fuzzy system reliability is constructed. The performance of fuzzy confidence interval is considered based on the coverage probability and the expected length. In order to obtain the fuzzy system reliability, the fuzzy sets theory is applied to the system reliability problem when dealing with uncertainties in the RMSS. The fuzzy number with a triangular membership function is used for constructing the fuzzy failure rate and the fuzzy repair rate in the fuzzy reliability for the RMSS. The result shows that the good interval estimator for the fuzzy confidence interval is the obtained coverage probabilities the expected confidence coefficient with the narrowest expected length. The model presented herein is an effective estimation method when the sample size is n ≥ 100. In addition, the optimal α-cut for the narrowest lower expected length and the narrowest upper expected length are considered. PMID:24987728
Fuzzy Adaptive Control System of a Non-Stationary Plant
Nadezhdin, Igor S.; Goryunov, Alexey G.; Manenti, Flavio
2016-08-01
This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.
Automatic control of biomass gasifiers using fuzzy inference systems
Energy Technology Data Exchange (ETDEWEB)
Sagues, C. [Universidad de Zaragoza (Spain). Dpto. de Informatica e Ingenieria de Sistemas; Garcia-Bacaicoa, P.; Serrano, S. [Universidad de Zaragoza (Spain). Dpto. de Ingenieria Quimica y Medio Ambiente
2007-03-15
A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated. (author)
Automatic control of biomass gasifiers using fuzzy inference systems.
Sagüés, C; García-Bacaicoa, P; Serrano, S
2007-03-01
A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.
Li, Shih-Yu; Tam, Lap-Mou; Tsai, Shang-En; Ge, Zheng-Ming
2015-09-11
Ge and Li proposed an alternative strategy to model and synchronize two totally different nonlinear systems in the end of 2011, which provided a new version for fuzzy modeling and has been applied to several fields to simplify their modeling works and solve the mismatch problems [1]-[17]. However, the proposed model limits the number of nonlinear terms in each equation so that this model could not be used in all kinds of nonlinear dynamic systems. As a result, in this paper, a more efficient and comprehensive advanced-Ge-Li fuzzy model is given to further release the limitation and improve the effectiveness of the original one. The novel fuzzy model can be applied to all kinds of complex nonlinear systems--this is the universal strategy and only m x 2 fuzzy rules as well as two linear subsystems are needed to simulate nonlinear behaviors (m is the number of states in a nonlinear dynamic system), whatever the nonlinear terms are copious or complicated. Further, the fuzzy synchronization of two nonlinear dynamic systems with totally distinct structures can be achieved via only two sets of control gains designed through the novel fuzzy model as well as its corresponding fuzzy synchronization scheme. Two complicated dynamic systems are designed to be the illustrations, Mathieu-Van der pol system with uncertainties and Quantum-cellular neural networks nano system with uncertainties, to show the effectiveness and feasibility of the novel fuzzy model.
Robust controller for a class of uncertain switched fuzzy systems
Institute of Scientific and Technical Information of China (English)
YANG Hong; ZHAO Jun
2007-01-01
A robustness control of uncertain switched fuzzy systems is presented.Using the switching technique and the Lyapunov function method,a continuous state feedback controller is built to ensure that for all allowable uncertainties the relevant closed-loop system is asymptotically stable.Furthermore,a switching strategy that achieves system global asymptotic stability of the uncertain switched fuzzy system is given.In this model,each subsystem of the switched system is an uncertain fuzzy system,and a common parallel distributed compensation controller is presented.The main condition is given in the form of convex combinations which are more solvable.This method transforms a certain switched system and has strong robustness for various system parameters.Simulations show the feasibility and the effectiveness of this method.
Directory of Open Access Journals (Sweden)
Zahra Mohammadi
2011-07-01
Full Text Available This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.
Expert systems guide biological phosphorus removal
Energy Technology Data Exchange (ETDEWEB)
Krichten, D.J.; Wilson, K.D.; Tracy, K.D. (Air Products and Chemicals, Inc., Allentown, PA (United States))
1991-10-01
There is a large body of knowledge regarding optimum control strategies for new secondary wastewater treatment technology using an anaerobic selector to provide biological phosphorus removal. However, because the selector technology is new and the concepts differ somewhat from those used in conventional activated sludge wastewater treatment, a method of communicating this knowledge to plant operators is needed. Traditional methods such as classroom training and operating manuals are of limited effectiveness. The commonplace availability and low cost of the personal computer (PC) makes it practical to use a computer program to communicate the type of information required to control a wastewater treatment plant. Knowledge-based systems technology, commonly referred to as expert systems (ES) technology, is easy to use, provides useful information regarding a consistent control strategy, relieves the anxiety associated with learning a new process,' and provides instruction for inexperienced personnel. ES technology does not require special formatted input and is therefore easily accessible. All information required by the program is readily available through routine laboratory analysis, common plant instrumentation, or direct user observation. The program was designed for all levels of computer users and will run on all IBM-compatible or Apple MacIntosh systems.
Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems
Directory of Open Access Journals (Sweden)
Rashmi Malhotra
2015-02-01
Full Text Available A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, case-based systems, or simply rules that have been developed through experience. Managers need a decision-making approach that is robust, competent, effective, efficient, and integrative to handle the multi-dimensional organizational entities. The decision maker deals with multiple players in an organization such as products, customers, competitors, location, geographic structure, scope, internal organization, and cultural dimension [46]. Sound decisions include two important concepts: efficiency (return on invested resources and effectiveness (reaching predetermined goals. However, quite frequently, the decision maker cannot simultaneously handle data from different sources. Hence, we recommend that managers analyze different aspects of data from multiple sources separately and integrate the results of the analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA and fuzzy logic. DEA evaluates and measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand fuzzy logic's main strength lies in handling imprecise data. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies. A major advantage of the DEA approach is that it clearly identifies the
Fuzzy logic in indoor position determination system
Directory of Open Access Journals (Sweden)
Michał Socha
2016-12-01
Full Text Available The article outlines how to use the convergence of collections to determine the position of a mobile device based on the WiFi radio signal strength with the use of fuzzy sets. The main aim is the development of the method for indoor position determination based on existing WiFi network infrastructure indoors. The approach is based on the WiFi radio infrastructure existing inside the buildings and requires operating mobile devices such as smartphones or tablets. An SQL database engine is also necessary as a widespread data interface. The SQL approach is not limited to the determination of the position but also to the creation of maps in which the system defining the position of the mobile device will operate. In addition, implementation issues are presented along with the distribution of the burden of performing calculations and the benefits of such an approach for determining the location. The authors describe how to decompose the task of determining the position in a client-server architecture.
Fuzzy Logic Temperature Control System For The Induction Furnace
Directory of Open Access Journals (Sweden)
Lei Lei Hnin
2015-08-01
Full Text Available This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.
Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.
Nagarale, Ravindrakumar M; Patre, B M
2014-05-01
This paper deals with the robust asymptotic stabilization for a class of nonlinear singularly perturbed systems using the fuzzy sliding mode control technique. In the proposed approach the original system is decomposed into two subsystems as slow and fast models by the singularly perturbed method. The composite fuzzy sliding mode controller is designed for stabilizing the full order system by combining separately designed slow and fast fuzzy sliding mode controllers. The two-time scale design approach minimizes the effect of boundary layer system on the full order system. A stability analysis allows us to provide sufficient conditions for the asymptotic stability of the full order closed-loop system. The simulation results show improved system performance of the proposed controller as compared to existing methods. The experimentation results validate the effectiveness of the proposed controller.
Uncertainty modeling in vibration, control and fuzzy analysis of structural systems
Halder, Achintya; Ayyub, Bilal M
1997-01-01
This book gives an overview of the current state of uncertainty modeling in vibration, control, and fuzzy analysis of structural and mechanical systems. It is a coherent compendium written by leading experts and offers the reader a sampling of exciting research areas in several fast-growing branches in this field. Uncertainty modeling and analysis are becoming an integral part of system definition and modeling in many fields. The book consists of ten chapters that report the work of researchers, scientists and engineers on theoretical developments and diversified applications in engineering sy
Institute of Scientific and Technical Information of China (English)
丁栋虹; 黄胜兰; 陈志; 宋怡; 黄秀丽
2014-01-01
By constructing the model of fuzzy expert system , this paper explores the optimal matching between six leadership styles ( spiritual type , coaching type , relational type , democratic type , forerunning type and com-manding type)and four organizational situation elements (organizational culture, subordinates’ maturity, leader-subordinate relationship and the uncertainty of the environment ) .Moreover , applying the model to analyze a pri-vate company of apparel external trade , we find that spiritual leadership would be in favour of the development of the company when the value of environmental uncertainty is larger and the values of organizational culture , subor-dinates ’ maturity and leader-subordinate relation are smaller .The conclusion demonstrates the descriptive power of the model , remedies the shortcoming of traditional qualitative studies , and will provide some suggestions for leaders who need to transform a fixed leadership style according to specific situations .%通过构建模糊专家系统模型，研究六类领导风格（愿景型、教练型、关系型、民主型、领跑型和命令型）与四种组织情境要素（组织文化、下属成熟度、领导-下属关系和环境不确定性）的最优匹配。并运用该模型对一家民营服装外贸公司进行了实证分析，发现该公司面临的环境不确定性值较大，而组织文化、下属成熟度和领导-下属关系的值相对较小，领导者采取愿景型领导风格更有利于企业的发展。结论表明了该模型的有效性，弥补了传统研究定性分析的不足，并对领导者根据具体组织情境转变单一的领导风格具有启示作用。
Horse-Expert: An aided expert system for diagnosing horse diseases.
Qin, H; Xiao, J; Gao, X; Wang, H
2016-12-01
In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been developed for diseases diagnosis, but few for horse diseases diagnosis have been studied in depth. This paper presents the design and development of a computer-aided expert system for diagnosing horse diseases. We suggest an approach for diagnosis of horse diseases based on the analysis of diagnostic characteristics and the experiential knowledge of domain experts. It is based on using evidence-weighted uncertainty reasoning theory, which is a combination of evidence theory and an uncertainty pass algorithm of confidence factors. It enables drawing of inferences with atypical clinical signs and the uncertainty of the user's subjective understanding. It reduces the influence of subjective factors on diagnostic accuracy. The system utilizes a user friendly interface for users and requests a confidence factor from users when feedback is given to the system. Horse-Expert combines the confidence factors with weight factors assigned to clinical signs by experts during the knowledge acquisition process to make diagnostic conclusions. The system can diagnose 91 common horse diseases, and provides suggestions for appropriate treatment options. In addition, users can check the medical record through statistical charts. The system has been tested in seven demonstration areas of Xinjiang province in northwestern China. By constantly maintaining and updating the knowledge base, the system has potential application in veterinary practice.
Directory of Open Access Journals (Sweden)
Dejan Mirčetić
2016-08-01
Full Text Available The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES models are created using several machine learning (ML models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS and classification and regression trees (CART are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement incargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.
Handbook of VLSI chip design and expert systems
Schwarz, A F
1993-01-01
Handbook of VLSI Chip Design and Expert Systems provides information pertinent to the fundamental aspects of expert systems, which provides a knowledge-based approach to problem solving. This book discusses the use of expert systems in every possible subtask of VLSI chip design as well as in the interrelations between the subtasks.Organized into nine chapters, this book begins with an overview of design automation, which can be identified as Computer-Aided Design of Circuits and Systems (CADCAS). This text then presents the progress in artificial intelligence, with emphasis on expert systems.
Fault Detection of Computer Communication Networks Using an Expert System
Directory of Open Access Journals (Sweden)
Ibrahiem M.M. El Emary
2005-01-01
Full Text Available The main objective of this study was to build an expert system for assisting the network administrator in his work of management and administration of the computer communication network. Theory of operation of the proposed expert system depends on using a time series model capable of forecasting the various performance parameters as: delay, utilization and collision frequency. When the expert system finds a difference (with certain tolerance between the predicted value and the measured value, it informs the network administrator that there exist problems in his network either in the switch or link or router. We examine two types of network by our proposed expert system, the first one is called token bus while the second one is called token ring. When we run our expert system on these two types of computer networks, actually the expert system captures the problem when there exists an excess deviation from the network performance parameters.
Applications of Fuzzy Sliding Mode Control for a Gyroscope System
Directory of Open Access Journals (Sweden)
Shih-Chung Chen
2013-01-01
Full Text Available The study proposed the application of the fuzzy sliding mode for a gyroscope system status control. The state response analysis of the gyroscope system revealed highly nonlinear and chaotic subharmonic motions of 2T during state formation. The current study discussed the use of tracking control on the sliding mode control and fuzzy sliding mode control of a gyroscope control system. Consequently, the gyroscope system drives from chaotic motion to periodic motion. The numerical simulation results confirm that the proposed controller provides good system stability and convergence without chattering phenomena.
Expert System Techniques and Applications in AEC-Design
DEFF Research Database (Denmark)
Andersen, Tom
This part of a book presents expert system techniques applicable to building design and construction, and it reports and evaluates on systems developed in thar domain.......This part of a book presents expert system techniques applicable to building design and construction, and it reports and evaluates on systems developed in thar domain....
Fuzzy Controllers for a Gantry Crane System with Experimental Verifications
Directory of Open Access Journals (Sweden)
Naif B. Almutairi
2016-01-01
Full Text Available The control problem of gantry cranes has attracted the attention of many researchers because of the various applications of these cranes in the industry. In this paper we propose two fuzzy controllers to control the position of the cart of a gantry crane while suppressing the swing angle of the payload. Firstly, we propose a dual PD fuzzy controller where the parameters of each PD controller change as the cart moves toward its desired position, while maintaining a small swing angle of the payload. This controller uses two fuzzy subsystems. Then, we propose a fuzzy controller which is based on heuristics. The rules of this controller are obtained taking into account the knowledge of an experienced crane operator. This controller is unique in that it uses only one fuzzy system to achieve the control objective. The validity of the designed controllers is tested through extensive MATLAB simulations as well as experimental results on a laboratory gantry crane apparatus. The simulation results as well as the experimental results indicate that the proposed fuzzy controllers work well. Moreover, the simulation and the experimental results demonstrate the robustness of the proposed control schemes against output disturbances as well as against uncertainty in some of the parameters of the crane.
Expert System for Minefield Site Prediction. Phase 1.
1988-02-01
2.2111- .25 Jlill 1 MICROCOPY RESOLUTION TLST CHART % %R( % % % % % ko , %% % - Af-A -:A.ZA .A r. ETL-0492 Expert system for minefield site...1. TITLE (Include Security Gassfication) EXPERT SYSTEM FOR MINEFIELD SITE PREDICTION FIRST YEAR REPORT r.. Z. PERSONAL AUTHOR(S) Dillencourt, Michael...identify by block number)FIELD GROUP L SUB-GROUP I Expert System ’ LMinefield,8ite ,rediction - * Quadtree,CTeraiin--nalysis,.t 19, ABSTRACT (Continue on
Application of the Agent in Agricultural Expert System Inspection Software
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
In reference to the status quo of research and the application of the agricultural expert system, this paper analyzes problems existing in the current development, and puts forward the idea of research and development for agriculturespecific software. The agent application is discussed, and an agent-based Agricultural Expert System Inspection Tool is constructed. In addition, this paper addresses the outlook in application, potential problems and the development trend of multi-agent-based inspection software for the agricultural expert system.
A Hybrid Architecture for Web-based Expert Systems
Neil Dunstan
2012-01-01
A recent technique is to represent the knowledge base of an expert system in XML format. XML parsers are then used to convert XML data into expert system language code. The code is executed or interpreted when providing responses to user queries. Web-based expert system (WBES) architectures may be characterized according to where the application knowledge base resides. Applications of both client and server-sided WBES architectures appear in the literature. A hybrid architecture is proposed w...
Adaptive Fuzzy Dynamic Surface Control for Uncertain Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
Xiao-Yuan Luo; Zhi-Hao Zhu; Xin-Ping Guan
2009-01-01
In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of "explosion of complexity" in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globaily uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller (SMC). Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.
Expert system for management of urinary incontinence in women.
Gorman, R.
1995-01-01
The purpose of this nursing informatics and outcomes research study was to determine the effectiveness of an expert system for disseminating knowledge to ambulatory women health care consumers with urinary incontinence. Clinical knowledge from the Agency for Health Care Policy and Research (AHCPR) patient guideline for urinary incontinence and research literature for behavioral treatments provided the knowledge base for the expert system. Two experimental groups (booklet and expert system) and one control group were utilized. Study results suggest the use of an expert system as one effective communication means for disseminating clinical information in an advisory capacity to ambulatory women with urinary incontinence. PMID:8563340
Three CLIPS-based expert systems for solving engineering problems
Parkinson, W. J.; Luger, G. F.; Bretz, R. E.
1990-01-01
We have written three expert systems, using the CLIPS PC-based expert system shell. These three expert systems are rule based and are relatively small, with the largest containing slightly less than 200 rules. The first expert system is an expert assistant that was written to help users of the ASPEN computer code choose the proper thermodynamic package to use with their particular vapor-liquid equilibrium problem. The second expert system was designed to help petroleum engineers choose the proper enhanced oil recovery method to be used with a given reservoir. The effectiveness of each technique is highly dependent upon the reservoir conditions. The third expert system is a combination consultant and control system. This system was designed specifically for silicon carbide whisker growth. Silicon carbide whiskers are an extremely strong product used to make ceramic and metal composites. The manufacture of whiskers is a very complicated process. which to date. has defied a good mathematical model. The process was run by experts who had gained their expertise by trial and error. A system of rules was devised by these experts both for procedure setup and for the process control. In this paper we discuss the three problem areas of the design, development and evaluation of the CLIPS-based programs.
Fuzzy logic based variable speed wind generation system
Energy Technology Data Exchange (ETDEWEB)
Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.
1996-12-31
This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.
First Priority, an expert system for prioritizing
Energy Technology Data Exchange (ETDEWEB)
Morton, G.R.; Hopson, P.C.
1991-01-01
Prioritizing lists of diverse entities such as projects, tasks, documents, recommendations or physical locations is a necessary part of business at DOE facilities. A key issue is whether or not this necessary and problematic activity of prioritizing is performed in a methodical, defensible and traceable manner, especially when the goals are hard to compare and measure. Sound methods of prioritizing are often not employed because of their complexity or difficulty in implementation. To overcome these problems, WSRC is developing an expert system. First Priority, which will provide individuals or committees a comprehensive process for prioritizing lists of any sort. A set of windows, editors, and pull-down menus guide the user in building and modifying an (inverted) weighted tree structure which represents the goals the prioritization is to advance. The process of building this structure is divided into four stages which are generally followed in order. These stages are: building the goal tree, ordering the goal tree nodes, weighting the goal tree nodes, and designing measurement methods for each leaf node. Based on the resultant structure an evaluation module is generated to evaluate the items of the list. This list is then prioritized and grouped into user-defined categories, taking into account cost or other resources. Additional First Priority tools provide sensitivity analysis, graphical displays of data, and reporting.
First Priority, an expert system for prioritizing
Energy Technology Data Exchange (ETDEWEB)
Morton, G.R.; Hopson, P.C.
1991-12-31
Prioritizing lists of diverse entities such as projects, tasks, documents, recommendations or physical locations is a necessary part of business at DOE facilities. A key issue is whether or not this necessary and problematic activity of prioritizing is performed in a methodical, defensible and traceable manner, especially when the goals are hard to compare and measure. Sound methods of prioritizing are often not employed because of their complexity or difficulty in implementation. To overcome these problems, WSRC is developing an expert system. First Priority, which will provide individuals or committees a comprehensive process for prioritizing lists of any sort. A set of windows, editors, and pull-down menus guide the user in building and modifying an (inverted) weighted tree structure which represents the goals the prioritization is to advance. The process of building this structure is divided into four stages which are generally followed in order. These stages are: building the goal tree, ordering the goal tree nodes, weighting the goal tree nodes, and designing measurement methods for each leaf node. Based on the resultant structure an evaluation module is generated to evaluate the items of the list. This list is then prioritized and grouped into user-defined categories, taking into account cost or other resources. Additional First Priority tools provide sensitivity analysis, graphical displays of data, and reporting.
Row Reduced Echelon Form for Solving Fully Fuzzy System with Unknown Coefficients
Directory of Open Access Journals (Sweden)
Ghassan Malkawi
2014-08-01
Full Text Available This study proposes a new method for finding a feasible fuzzy solution in positive Fully Fuzzy Linear System (FFLS, where the coefficients are unknown. The fully fuzzy system is transferred to linear system in order to obtain the solution using row reduced echelon form, thereafter; the crisp solution is restricted in obtaining the positive fuzzy solution. The fuzzy solution of FFLS is included crisp intervals, to assign alternative values of unknown entries of fuzzy numbers. To illustrate the proposed method, numerical examples are solved, where the entries of coefficients are unknown in right or left hand side, to demonstrate the contributions in this study.
Rule weights in a neuro-fuzzy system with a hierarchical domain partition
National Research Council Canada - National Science Library
Krzysztof Siminski
2010-01-01
Rule weights in a neuro-fuzzy system with a hierarchical domain partition The paper discusses the problem of rule weight tuning in neuro-fuzzy systems with parameterized consequences in which rule...
Indirect Adaptive Fuzzy and Impulsive Control of Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
Hai-Bo Jiang
2010-01-01
The problem of indirect adaptive fuzzy and impulsive control for a class of nonlinear systems is investigated.Based on the approximation capability of fuzzy systems,a novel adaptive fuzzy and impulsive control strategy with supervisory controller is developed.With the help of a supervisory controller,global stability of the resulting closed-loop system is established in the sense that all signals involved are uniformly bounded.Furthermore,the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error.By the generalized Barbalat's lemma,the tracking error between the output of the system and the reference signal is proved to be convergent to zero asymptotically.Simulation results illustrate the effectiveness of the proposed approach.
New Asymmetric Fuzzy PID Control for Pneumatic Position Control System
Institute of Scientific and Technical Information of China (English)
薛阳; 彭光正; 范萌; 伍清河
2004-01-01
A fuzzy control algorithm of asymmetric fuzzy strategy is introduced for a servo-pneumatic position system. It can effectively solve the difficult problems of single rod low friction cylinders, which are mainly caused by asymmetric structures and different friction characteristics in two directions. On the basis of this algorithm, a traditional PID control is used to improve dynamic performance. Furthermore, a new asymmetric fuzzy PID control with α factor is advanced to improve the self-adaptability and robustness of the system. Both the theoretical analyses and experimental results prove that, with this control strategy, the dynamic performance of the system can be greatly improved. The system using this control algorithm has strong robustness and it obtains desired overshoot and repeatability in both transient and steady-state responses.
Expert System Based on Data Mining and Neural Networks
Institute of Scientific and Technical Information of China (English)
NI Zhi-wei; JIA Rui-yu
2001-01-01
On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural expert system to find some unknown useful knowledge and get some satisfactory results.
A design for testability expert system for silicon compilers
van Riessen, R.P.; van Riessen, R.P.; Kerkhoff, Hans G.; Janssen, J.M.J.
1991-01-01
This paper describes a design-for-testability expert system for the selection of the most appropriate test method for every macro within an IC. The interface with the system designer is user-friendly and together with an efficient search mechanism this expert system can be used as a framework for
AN EXPERT SYSTEM MODEL FOR THE SELECTION OF TECHNICAL PERSONNEL
Directory of Open Access Journals (Sweden)
Emine COŞGUN
2005-03-01
Full Text Available In this study, a model has been developed for the selection of the technical personnel. In the model Visual Basic has been used as user interface, Microsoft Access has been utilized as database system and CLIPS program has been used as expert system program. The proposed model has been developed by utilizing expert system technology. In the personnel selection process, only the pre-evaluation of the applicants has been taken into consideration. Instead of replacing the expert himself, a decision support program has been developed to analyze the data gathered from the job application forms. The attached study will assist the expert to make faster and more accurate decisions.
A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems
Institute of Scientific and Technical Information of China (English)
王攀; 徐承志; 冯珊; 徐爱华
2002-01-01
This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.
ASSESSING THE SUSTAINABILITY OF AGRICULTURAL PRODUCTION SYSTEMS USING FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Moslem Sami
2013-09-01
Full Text Available First stage for attaining sustainability in a system is the measurement of current state of sustainability. Indicators are widely used as tools for measurement of sustainability. In this study, a comprehensive index was proposed to measure sustainability in agricultural production systems. This index takes advantage of fuzzy logic to combine all six indexes which were selected as the representative of three dimensions of sustainability. A set of models and sub-models based on the fuzzy inference system were employed to define the index. A case study conducted in two large production farms of maize and wheat, in Iran, proved the feasibility and usability of the model.
Fuzzy synthetic assessment of building fire safety system
Institute of Scientific and Technical Information of China (English)
YANG Gao-shang; PENG Li-min
2005-01-01
A multistage assessment index set is chosen based on the analysis of building fire safety system, whereby the weight of each index is determined through an analy tie.hierarchy process; a fuzzy synthetic assessment model for the building fire safety system is constructed, and the quantified result was obtained by using hierarchy parameter judgment. This fuzzy synthetic assessment method can quantify assessment result of the building fire safety system, so thatthe fire precautions may be accurately adopted, and the serious potential risk may be avoided. The application shows that this method possesses both objectivity and feasibility.
Lagrangian Fuzzy Dynamics of Physical and Non-Physical Systems
Sandler, Uziel
2014-01-01
In this paper, we show how to study the evolution of a system, given imprecise knowledge about the state of the system and the dynamics laws. Our approach is based on Fuzzy Set Theory, and it will be shown that the \\emph{Fuzzy Dynamics} of a $n$-dimensional system is equivalent to Lagrangian (or Hamiltonian) mechanics in a $n+1$-dimensional space. In some cases, however, the corresponding Lagrangian is more general than the usual one and could depend on the action. In this case, Lagrange's eq...
Arsad, Roslah; Nasir Abdullah, Mohammad; Alias, Suriana; Isa, Zaidi
2017-09-01
Stock evaluation has always been an interesting problem for investors. In this paper, a comparison regarding the efficiency stocks of listed companies in Bursa Malaysia were made through the application of estimation method of Data Envelopment Analysis (DEA). One of the interesting research subjects in DEA is the selection of appropriate input and output parameter. In this study, DEA was used to measure efficiency of stocks of listed companies in Bursa Malaysia in terms of the financial ratio to evaluate performance of stocks. Based on previous studies and Fuzzy Delphi Method (FDM), the most important financial ratio was selected. The results indicated that return on equity, return on assets, net profit margin, operating profit margin, earnings per share, price to earnings and debt to equity were the most important ratios. Using expert information, all the parameter were clarified as inputs and outputs. The main objectives were to identify most critical financial ratio, clarify them based on expert information and compute the relative efficiency scores of stocks as well as rank them in the construction industry and material completely. The methods of analysis using Alirezaee and Afsharian’s model were employed in this study, where the originality of Charnes, Cooper and Rhodes (CCR) with the assumption of Constant Return to Scale (CSR) still holds. This method of ranking relative efficiency of decision making units (DMUs) was value-added by the Balance Index. The interested data was made for year 2015 and the population of the research includes accepted companies in stock markets in the construction industry and material (63 companies). According to the ranking, the proposed model can rank completely for 63 companies using selected financial ratio.
MOORE: A prototype expert system for diagnosing spacecraft problems
Howlin, Katherine; Weissert, Jerry; Krantz, Kerry
1988-01-01
MOORE is a rule-based, prototype expert system that assists in diagnosing operational Tracking and Data Relay Satellite (TDRS) problems. It is intended to assist spacecraft engineers at the TDRS ground terminal in trouble shooting problems that are not readily solved with routine procedures, and without expert counsel. An additional goal of the prototype system is to develop in-house expert system and knowledge engineering skills. The prototype system diagnoses antenna pointing and earth pointing problems that may occur within the TDRS Attitude Control System (ACS). Plans include expansion to fault isolation of problems in the most critical subsystems of the TDRS spacecraft. Long term benefits are anticipated with use of an expert system during future TDRS programs with increased mission support time, reduced problem solving time, and retained expert knowledge and experience. Phase 2 of the project is intended to provide NASA the necessary expertise and capability to define requirements, evaluate proposals, and monitor the development progress of a highly competent expert system for NASA's Tracking Data Relay Satellite. Phase 2 also envisions addressing two unexplored applications for expert systems, spacecraft integration and tests (I and T) and support to launch activities. The concept, goals, domain, tools, knowledge acquisition, developmental approach, and design of the expert system. It will explain how NASA obtained the knowledge and capability to develop the system in-house without assistance from outside consultants. Future plans will also be presented.
Fuzzy fractional order sliding mode controller for nonlinear systems
Delavari, H.; Ghaderi, R.; Ranjbar, A.; Momani, S.
2010-04-01
In this paper, an intelligent robust fractional surface sliding mode control for a nonlinear system is studied. At first a sliding PD surface is designed and then, a fractional form of these networks PDα, is proposed. Fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. To reduce the chattering phenomenon in sliding mode control (SMC), a fuzzy logic controller is used to replace the discontinuity in the signum function at the reaching phase in the sliding mode control. For the problem of determining and optimizing the parameters of fuzzy sliding mode controller (FSMC), genetic algorithm (GA) is used. Finally, the performance and the significance of the controlled system two case studies (robot manipulator and coupled tanks) are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results signify performance of genetic-based fuzzy fractional sliding mode controller.
A TWO-PHASE APPROACH TO FUZZY SYSTEM IDENTIFICATION
Institute of Scientific and Technical Information of China (English)
Ta-Wei HUNG; Shu-Cherng FANG; Henry L.W.NUTTLE
2003-01-01
A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a coIlection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning)is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.
Advanced Fuzzy Logic Based Admission Control for UMTS System
Directory of Open Access Journals (Sweden)
P. Kejik
2010-12-01
Full Text Available The capacity of CDMA (Code Division Multiple Access systems is interference limited. Therefore radio resources management (RRM functions are used. They are responsible for supplying optimum coverage, ensuring efficient use of physical resources, and providing the maximum planned capacity. This paper deals with admission control techniques for UMTS (Universal Mobile Telecommunication System. A UMTS system model and four fuzzy logic based admission control algorithms are presented in this paper. Two new versions of fuzzy logic based admission control algorithms are presented there. All algorithms are mutually compared via simulations. Simulations show that the novel advanced fuzzy algorithm outperforms the other simulated algorithms (in terms of blocking probability, dropping probability and the number of active UEs in cell.
The design of thermoelectric footwear heating system via fuzzy logic.
Işik, Hakan; Saraçoğlu, Esra
2007-12-01
In this study, Heat Control of Thermoelectric Footwear System via Fuzzy Logic has been implemented in order to use efficiently in cold weather conditions. Temperature control is very important in domestic as well as in many industrial applications. The final product is seriously affected from the changes in temperature. So it is necessary to reach some desired temperature points quickly and avoid large overshoot. Here, fuzzy logic acts an important role. PIC 16F877 microcontroller has been designed to act as fuzzy logic controller. The designed system provides energy saving and has better performance than proportional control that was implemented in the previous study. The designed system takes into consideration so appropriate parameters that it can also be applied to the people safely who has illnesses like diabetes, etc.
Directory of Open Access Journals (Sweden)
Guo Haigang
2012-01-01
Full Text Available Combining adaptive fuzzy sliding mode control with fuzzy or variable universe fuzzy switching technique, this study develops two novel direct adaptive schemes for a class of MIMO nonlinear systems with uncertainties and external disturbances. The proposed control schemes consist of fuzzy equivalent control terms, fuzzy switching control terms (in scheme one or variable universe fuzzy switching control terms (in scheme two, and compensation control terms. The compensation control terms are used to relax the assumption on fuzzy approximation error. Based on Lyapunov stability theory, the parameters update laws are adaptively tuned online and the global asymptotic stability of the closed-loop system can be guaranteed. The major contribution of this study is to develop a novel framework for designing direct adaptive fuzzy sliding mode control scheme facing model uncertainties and external disturbances. The derived schemes can effectively solve the chattering problem and the equivalent control calculation in that environment. Simulation results performed on a two-link robotic manipulator demonstrate the feasibility of the proposed control schemes.
Design and Analysis of Fuzzy Metagraph Based Data Structures
A.Thirunavukarasu; Dr. S. Uma Maheswari
2012-01-01
Fuzzy metagraph is an emerging technique used in the design of many information processing systems like transaction processing systems, decision support systems, and workflow systems. Very often, evena carefully chosen graph data structure could be improvised to provide more efficiency in terms of time complexity or space complexity or both. In this paper, a well-designed fuzzy metagraph is proposed and distinct matrices have been developed to reduce the time-complexity. Fuzzy Expert System (...
Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges
Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu
2016-06-01
In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.
Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges
Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu
2016-09-01
In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.
Testing validation tools on CLIPS-based expert systems
Chang, C. L.; Stachowitz, R. A.; Combs, J. B.
1991-01-01
The Expert Systems Validation Associate (EVA) is a validation system which was developed at the Lockheed Software Technology Center and Artificial Intelligence Center between 1986 and 1990. EVA is an integrated set of generic tools to validate any knowledge-based system written in any expert system shell such as C Language Integrated Production System (CLIPS), ART, OPS5, KEE, and others. Many validation tools have been built in the EVA system. In this paper, we describe the testing results of applying the EVA validation tools to the Manned Maneuvering Unit (MMU) Fault Diagnosis, Isolation, and Reconfiguration (FDIR) expert system, written in CLIPS, obtained from the NASA Johnson Space Center.
Studying on the Fuzzy-QFD System Based on Database Class Encapsulation Technology
Institute of Scientific and Technical Information of China (English)
FANG Xifeng; ZHANG Shengwen; LU Yuping; WU Hongtao
2006-01-01
Complicated product QFD system design information including design and manufacturing, operation and maintenance as well as relative supply information, all are tightly related to the product life cycle cooperative design and the process of establishing the QFD system. In the early stage of product design, we can only get the fuzzy and unreliable information. With design going, the fuzzy and unreliable information become less and less. The defect of the traditional QFD is not deal with the fuzzy contents very well. Adopt database class encapsulation and fuzzy inference technology, and then discuss the realization of QFD system based on VFP database. The structure of the fuzzy QFD system based on database class's encapsulation is built and the work flow of fuzzy algorithm based on VFP software is presented. In the analysis of fuzzy QFD process, fuzzy inference is adopted. A developed prototype system and an example have verified some presented techniques and the research results are the basis of the future development.
Novel Approach to Fuzzy Logic Controller Design for Systems With Deadzones
Kim, Jong-Hwan; Park, Jong-Hwan; Lee, Seon-Woo; Chong, Edwin K. P.
1992-01-01
Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a conventional fuzzy logic controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this report, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator...
The Principles of Designing an Expert System in Teaching Mathematics
Salekhova, Lailya; Nurgaliev, Albert; Zaripova, Rinata; Khakimullina, Nailya
2013-01-01
This study reveals general didactic concepts of the Expert Systems (ES) development process in the educational area. The proof of concept is based on the example of teaching the 8th grade Algebra subject. The main contribution in this work is the implementation of innovative approaches in analysis and processing of data by expert system as well as…
Expert System for Test Program Set Fault Candidate Selection
1989-09-01
This report describes an application of expert system technology to test program set (TPS) verification and validation. The goals of this project are...Keywords: Expert system , Artificial intelligence, Automatic test equipment (ATE), Test program set (TPS), Automatic test program generation (ATPG), Fault inspection, Verification and validation, TPS acceptance tool.
20 CFR 405.10 - Medical and Vocational Expert System.
2010-04-01
... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Medical and Vocational Expert System. 405.10 Section 405.10 Employees' Benefits SOCIAL SECURITY ADMINISTRATION ADMINISTRATIVE REVIEW PROCESS FOR ADJUDICATING INITIAL DISABILITY CLAIMS Introduction, General Description, and Definitions § 405.10 Medical and Vocational Expert System. (a) General....
Distributed Expert-Based Information Systems: An Interdisciplinary Approach.
Belkin, Nicholas J.; And Others
1987-01-01
Based on an international workshop held at Rutgers University, this article discusses problems and issues in the design, research, and implementation of distributed expert-based information systems (DEBIS). Information needs of end users are stressed, architectures for expert information retrieval systems are explored, and prototype models are…
An Expert System Adviser for Tourists Planning To Visit Thailand.
Kanchanosatha, Vinita
This document reports on an examination of the analysis, design, development, implementation, and evaluation of an interactive computer program. The program is called an expert system adviser, and is for tourists planning on visiting Thailand. The expert system contains well-organized information that provides detailed coverage of Thailand. The…
The BRIEFER project: using expert systems as theory construction tools.
Gingerich, W J; de Shazer, S
1991-06-01
This article describes the development of BRIEFER I and BRIEFER II, expert systems that advise the therapist on selecting, designing, and developing an intervention at the end of the first therapy session. The process of developing expert systems has aided us in describing what brief therapists do, in modeling the intervention design process, and in training brief therapists.
Integrated Knowledge Based Expert System for Disease Diagnosis System
Arbaiy, Nureize; Sulaiman, Shafiza Eliza; Hassan, Norlida; Afizah Afip, Zehan
2017-08-01
The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.
Genetic fuzzy system predicting contractile reactivity patterns of small arteries
DEFF Research Database (Denmark)
Tang, J; Sheykhzade, Majid; Clausen, B F;
2014-01-01
information. We developed a genetic fuzzy system (GFS) algorithm that is capable of learning all information in time-domain physiological data. Data on isometric force development of isolated small arteries were used as a framework for developing and optimizing a GFS. GFS performance was improved by several...... strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used...
Fuzzy-Immune PID Control for AMB Systems
Institute of Scientific and Technical Information of China (English)
SU Yixin; LI Xuan; ZHOU Zude; CHEN Youping; ZHANG Danhong
2006-01-01
In order to improve the dynamic performance of active magnetic bearing 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 control. 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 demonstrate that the active magnetic bearing system with the proposed controller has better dynamic performance and disturbance rejection ability than using the linear PID controller.
Performance evaluation of the distance education system with fuzzy logic
Armaǧan, Hamit; Yiǧit, Tuncay
2017-07-01
Distance education is a kind of education that brought together course advisor, student and educational materials in a different time and place through communicational technologies. In this educational system the success of education is directly related to audio, video and interaction. In this study, a model is created by using fuzzy logic with the success of distance education students and the components of distance education. This study is made by MATLAB fuzzy logic toolbox. Audio, video, educational technology, student achievement are used as parameters in the evaluation. System assessment is carried out depending on parameter.
Fault Detection in Systems-A Fuzzy Approach
Directory of Open Access Journals (Sweden)
Ashok Kumar
2004-04-01
Full Text Available The task of fault detection is important when dealing with failures of crucial nature. After detection of faults in a system, it is advisable to suggest maintenance action before occurrenceof a failure. Fault detection may be done by observing various symptoms of the system during its operational stage. Sometimes, symptoms cannot be quantified easily but can be expressedin linguistic terms. Since linguistic terms are fuzzy quantifiers, these can be represented by fuzzy numbers. In this paper, two cases have been discussed, where a fault likely to affect a particular systemlsystems, is detected. In the first case, this is done by means of a compositional rule of inference. The second case is based on modified similarity measure. For both these cases, linguistic terms have been expressed as trapezoidal fuzzy numbers
Application of genetic algorithms to tuning fuzzy control systems
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
Recent Advances in Interval Type-2 Fuzzy Systems
Castillo, Oscar
2012-01-01
This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hy-brid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We con-sider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.
Advanced Takagi‒Sugeno fuzzy systems delay and saturation
Benzaouia, Abdellah
2014-01-01
This monograph puts the reader in touch with a decade’s worth of new developments in the field of fuzzy control specifically those of the popular Takagi-Sugeno (T-S) type. New techniques for stabilizing control analysis and design based on multiple Lyapunov functions and linear matrix inequalities (LMIs), are proposed. All the results are illustrated with numerical examples and figures and a rich bibliography is provided for further investigation. Control saturations are taken into account within the fuzzy model. The concept of positive invariance is used to obtain sufficient asymptotic stability conditions for the fuzzy system with constrained control inside a subset of the state space. The authors also consider the non-negativity of the states. This is of practical importance in many chemical, physical and biological processes that involve quantities that have intrinsically constant and non-negative sign: concentration of substances, level of liquids, etc. Results for linear systems are then extended to l...
A Belief Rule-Based Expert System to Diagnose Influenza
DEFF Research Database (Denmark)
Hossain, Mohammad Shahadat; Khalid, Md. Saifuddin; Akter, Shamima
2014-01-01
). The RIMER approach can handle different types of uncertainties, both in knowledge representation, and in inference procedures. The knowledge-base of this system was constructed by using records of the real patient data along with in consultation with the Influenza specialists of Bangladesh. Practical case......, development and application of an expert system to diagnose influenza under uncertainty. The recently developed generic belief rule-based inference methodology by using the evidential reasoning (RIMER) approach is employed to develop this expert system, termed as Belief Rule Based Expert System (BRBES...... studies were used to validate the BRBES. The system generated results are effective and reliable than from manual system in terms of accuracy....
New approach to solve symmetric fully fuzzy linear systems
Indian Academy of Sciences (India)
P Senthilkumar; G Rajendran
2011-12-01
In this paper, we present a method to solve fully fuzzy linear systems with symmetric coefﬁcient matrix. The symmetric coefﬁcient matrix is decomposed into two systems of equations by using Cholesky method and then a solution can be obtained. Numerical examples are given to illustrate our method.
Using Expert Systems in the Management of Industrial Equipment Maintenance
IOAN CUCU; CODRUŢA DURA; IMOLA DRIGĂ
2009-01-01
The term “expert system” generally evokes new management techniques in various fields of activity. The definition of the expert systems in terms of their architecture reveals three basic elements: the knowledgebase containing specialized knowledge in a certain area, taken from the human expert in that field; the facts which include information related to the situation of management and data concerning a certain problem to be solved and the inference engine which is intended to exploit the set...
The Gas Resources Assessment Expert System of the Songliao Basin
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The gas resources assessment expert system is one of the advanced methods for appraising oil and gas resources. The establishment of a knowledge base is the focal task in developing the expert system. This paper presents a summary of the mechanism and the major controlling factors in the formation of gas pools in the southeast uplift of the Songliao basin. Then an appropriate assessment model is established for trapping the gas resources and a knowledge base built in the expert system to realize the model. By using the expert system to appraise the gas-bearing probability of 25 major traps of the Quantou and Denglouku Formations in the Shiwu-Dehui area, the authors have proved that the expert system is suitable for appraising traps in the Songliao basin and similar basins.
Evaluation of Combined Heat and Power (CHP Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS
Directory of Open Access Journals (Sweden)
Fausto Cavallaro
2016-06-01
Full Text Available Combined heat and power (CHP or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as “sustainable”, we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon’s entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS approach will be tested for this purpose. Shannon’s entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria—it does not require a decision-making (DM to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view.
Fuzzy Evidence in Identification, Forecasting and Diagnosis
Rotshtein, Alexander P
2012-01-01
The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems in order to identify nonlinear objects; that is, to build input-output models using expert and experimental information. The results of these identifications are used for direct and inverse fuzzy evidence in forecasting and diagnosis problem solving. The book is organised as follows: Chapter 1 presents the basic knowledge about fuzzy sets, genetic algorithms and neural nets necessary for a clear understanding of the rest of this book. Chapter 2 analyzes direct fuzzy inference based on fuzzy if-then rules. Chapter 3 is devoted to the tuning of fuzzy rules for direct inference using genetic algorithms and neural nets. Chapter 4 presents models and algorithms for extracting fuzzy rules from experimental data. Chapter 5 describes a method for solving fuzzy logic equations necessary for the inverse fuzzy inference in diagnostic systems. Chapters 6 and 7 are devoted to inverse fuzzy inference based on fu...
Using of Expert Systems in Electrodiagnostics of Large Electrical Machines
Directory of Open Access Journals (Sweden)
K. Záliš
2000-01-01
Full Text Available Several rule-based expert systems were developed for diagnostics of high voltage (HV insulation systems, especially for the evaluation of partial discharge (PD activity. Several rule-based expert systems were developed in the cooperation of top diagnostic workplaces of the Czech Republic for this purpose. The IZOLEX expert system evaluates diagnostic measurement data from commonly used off-line diagnostic methods for the diagnostics of HV insulation of rotating machines, non-rotating machines and insulating oils. The CVEX expert system evaluates the PD activity on HV electrical machines and equipment by means of an off-line measurement. The CVEXON expert system is for the evaluation of the discharge activity by on-line measurement and the ALTONEX expert system is the system for on-line monitoring of rotating machines. The complex project for the evaluation of a PD measurement on HV insulation systems has also been made. This complex evaluating system includes two parallel expert systems for the evaluation of a PD activity on HV electrical machines.
Efficient Fuzzy Logic Controller for Magnetic Levitation Systems
Directory of Open Access Journals (Sweden)
D. S. Shu’aibu
2016-12-01
Full Text Available Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system in air against gravity without using fixed structure for supporting is highly unstable and complex. In the previous research many techniques of stabilizing magnetic levitation systems were discussed. In this paper magnetic levitation controller using fuzzy logic is proposed. The proposed Fuzzy logic controller (FLC is designed, and developed using triangular membership function with 7×7 rules. The system model was implemented in MATLAB/SIMULINK and the system responses to Fuzzy controller with different input signals were investigated. Using unit step input signal, the proposed controller has a settling time of 0.35 secs, percentage overshoot of 0% and there is no oscillation. The proposed controller is validated with a model of an existing practical conventional proportional plus derivatives (PD controller. The PD controller has a settling time of 0.45 secs, percentage overshoot of 7% and with oscillation. Similarly, with sinusoidal input, the FLC has a phase shift and peak response of 0^0 and 0.9967 respectively, while PD controller has a phase shift and peak response of 24.48o and 0.9616 respectively. A disturbance signal was applied to the input of the control system. Fuzzy controller succeeded in rejecting the disturbance signal without further turning of the parameters whereby PD controller failed.
Hybrid TS fuzzy modelling and simulation for chaotic Lorenz system
Institute of Scientific and Technical Information of China (English)
Li De-Quan
2006-01-01
The projection of the chaotic attractor observed from the Lorenz system in the X-Z plane is like a butterfly, hence the classical Lorenz system is widely known as the butterfly attractor, and has served as a prototype model for studying chaotic behaviour since it was coined. In this work we take one step further to investigate some fundamental dynamic behaviours of a novel hybrid Takagi-Sugeno (TS) fuzzy Lorenz-type system, which is essentially derived from the delta-operator-based TS fuzzy modelling for complex nonlinear systems, and contains the original Lorenz system of continuous-time TS fuzzy form as a special case. By simply and appropriately tuning the additional parametric perturbations in the two-rule hybrid TS fuzzy Lorenz-type system, complex (two-wing) butterfly attractors observed from this system in the three dimensional (3D) X-Y-Z space are created, which have not yet been reported in the literature, and the forming mechanism of the compound structures have been numerically investigated.
Fuzzy stochastic neural network model for structural system identification
Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong
2017-01-01
This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
The Development of a Fuzzy Predictive Control System for Automotive Anti-lock Braking System
Institute of Scientific and Technical Information of China (English)
HUANGFU Shihui; BAO Xiangying
2006-01-01
This paper presents the model of one-tire kinetics、tires、the braking system and the model of control system. On virtual road, this paper builds a fuzzy predictive control system to insure the best attachment coefficient between tires and road. And it turns out to be that this fuzzy predictive control method has achieved good performances.
Bandemer, Hans
1992-01-01
Fuzzy data such as marks, scores, verbal evaluations, imprecise observations, experts' opinions and grey tone pictures, are quite common. In Fuzzy Data Analysis the authors collect their recent results providing the reader with ideas, approaches and methods for processing such data when looking for sub-structures in knowledge bases for an evaluation of functional relationship, e.g. in order to specify diagnostic or control systems. The modelling presented uses ideas from fuzzy set theory and the suggested methods solve problems usually tackled by data analysis if the data are real numbers. Fuzzy Data Analysis is self-contained and is addressed to mathematicians oriented towards applications and to practitioners in any field of application who have some background in mathematics and statistics.
A fuzzy neural network evolved by particle swarm optimization
Institute of Scientific and Technical Information of China (English)
PENG Zhi-ping; PENG Hong
2007-01-01
A cooperative system of a fuzzy logic model and a fuzzy neural network (CSFLMFNN) is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the model. Then PSO-CSFLMFNN is constructed by introducing particle swarm optimization (PSO) into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural network. The evolutionary fuzzy neural network implements accuracy fuzzy inference without rule matching. PSO-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment, in which the cooperative system is proved to be effective. It is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.
Application of Fuzzy Clustering in Modeling of a Water Hydraulics System
DEFF Research Database (Denmark)
Zhou, Jianjun; Kroszynski, Uri
2000-01-01
This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy mo...
An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
Directory of Open Access Journals (Sweden)
Manoj Kumar Jain
2012-04-01
Full Text Available Quite often the variables used in system analysis are qualitative in nature. They cannot be defined precisely, whereas software development for system analysis needs a mathematical framework with precise computations. It is not trivial to capture the uncertainty in the system.
Fuzzy sets provide us the facility to capture the uncertainty in the system. In normal crisp set where the membership of an element is always certain in a sense that it would be member or not of the given set. In contrast to this a membership functions or possibility (ranging from 0 to 1, including both values is assigned with each member. System analysis is done through system dynamics which is not very efficient. We present an efficient technique to generate expert system using fuzzy set. In our proposed approach five linguistic qualifiers are used for each variable, namely, Very Low (VL, Low (L, Medium (M, High (H, and Very High
(VH. We capture the influence or feedback in the system with the help of if then else rules and matrices are generated for them which are used for analysis. Complete methodology and its applicability are presented here.
The Expert System Designed to Improve Customer Satisfaction
Devi, P Isakki alias
2011-01-01
Customer Relationship Management becomes a leading business strategy in highly competitive business environment. It aims to enhance the performance of the businesses by improving the customer satisfaction and loyalty. The objective of this paper is to improve customer satisfaction on product's colors and design with the help of the expert system developed by using Artificial Neural Networks. The expert system's role is to capture the knowledge of the experts and the data from the customer requirements, and then, process the collected data and form the appropriate rules for choosing product's colors and design. In order to identify the hidden pattern of the customer's needs, the Artificial Neural Networks technique has been applied to classify the colors and design based upon a list of selected information. Moreover, the expert system has the capability to make decisions in ranking the scores of the colors and design presented in the selection. In addition, the expert system has been validated with a different...
Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine
Institute of Scientific and Technical Information of China (English)
JIA Jiong; ZHANG Hao-ran
2006-01-01
This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR,then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.
MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
2015-08-02
16. SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY STATEMENT 6...Instance AdaptiveNeuro-Fuzzy Inference System We introduce a novel adaptive neuro -fuzzy architecture based on the framework of Multiple Instance Fuzzy...Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS
Rough Set Fuzzy Optimum Selecting in Multidisciplinary System
Institute of Scientific and Technical Information of China (English)
LIU Xu-lin; SONG Bao-wei; WANG Jin-hua; CHEN Jie
2008-01-01
Scheme evaluation and selection is an optimum selecting and sequencing problem with multi-objective and multi- level. It can't follow single objective function or rule. Meanwhile, these objectives are coupled with each other and the at- tribution information is fuzzy also. It is necessary to find an effective evaluation method which can consider all conditions and restrictions. In this paper, AHP and rough set theory are applied to fuzzy optimization to determine important weight of each attribution. The rough set fuzzy optimum selection is used to eliminate the useless information. Autonomous un- derwater vehicle (AUV) is large-scah systems with many coupled design variables and objective functions. Their scheme evaluation and selection are very important, which relate to multiple factors, such as reliability;security, service time; the lifeeyele, etc. Results of application in torpedo design indicate that this method is feasible.
Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness
Directory of Open Access Journals (Sweden)
Animesh Biswas
2014-01-01
Full Text Available This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.
The Diagnostic Value of Skin Disease Diagnosis Expert System.
Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Arabkermany, Zahra; Gilasi, Hamidreza
2016-02-01
Evaluation is a necessary measure to ensure the effectiveness and efficiency of all systems, including expert systems. The aim of this study was to determine the diagnostic value of expert system for diagnosis of complex skin diseases. A case-control study was conducted in 2015 to determine the diagnostic value of an expert system. The study population included patients who were referred to Razi Specialized Hospital, affiliated to Tehran University of Medical Sciences. The control group was selected from patients without the selected skin diseases. Data collection tool was a checklist of clinical signs of diseases including pemphigus vulgaris, lichen planus, basal cell carcinoma, melanoma, and scabies. The sample size formula estimated 400 patients with skin diseases selected by experts and 200 patients without the selected skin diseases. Patient selection was undertaken with randomized stratified sampling and their sign and symptoms were logged into the system. Physician's diagnosis was determined as the gold standard and was compared with the diagnosis of expert system by SPSS software version 16 and STATA. Kappa statistics, indicators of sensitivity, specificity, accuracy and confidence intervals were calculated for each disease. An accuracy of 90% was considered appropriate. Comparing the results of expert system and physician's diagnosis at the evaluation stage showed an accuracy of 97.1%, sensitivity of 97.5% and specificity of 96.5% The Kappa test indicated a high agreement of 93.6%. The expert system can diagnose complex skin diseases. Development of such systems is recommended to identify all skin diseases.
Performance Enhancement of Intrusion Detection using Neuro - Fuzzy Intelligent System
Directory of Open Access Journals (Sweden)
Dr. K. S. Anil Kumar
2014-10-01
Full Text Available This research work aims at developing hybrid algorithms using data mining techniques for the effective enhancement of anomaly intrusion detection performance. Many proposed algorithms have not addressed their reliability with varying amount of malicious activity or their adaptability for real time use. The study incorporates a theoretical basis for improvement in performance of IDS using K-medoids Algorithm, Fuzzy Set Algorithm, Fuzzy Rule System and Neural Network techniques. Also statistical significance of estimates has been looked into for finalizing the best one using DARPA network traffic datasets.