A Sugeno-Type Fuzzy Expert System for Rough Turning

2013 ◽  
Vol 572 ◽  
pp. 597-600 ◽  
Author(s):  
Juho Ratava ◽  
Pasi Luukka ◽  
Mika Lohtander ◽  
Juha Varis
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Traditional Approach ◽  
Fuzzy Expert System ◽  
Measured Quantity ◽  
Functional Requirements ◽  
Quality Requirements ◽  
Intelligent Machining ◽  
Machining System ◽  
Rough Turning

This work describes a fuzzy expert system for rough turning. In order to automate unmanned turning, safety of the process must be ensured. In addition, any quality requirements should be fulfilled and, within these constraints, productivity maximized. The traditional approach in adaptive control of machining is to keep a measured quantity, such as power, within acceptable limits. However, there have been some studies measuring distinct phenomena in machining and identifying “cutting states” based on the phenomena. By identifying cutting states corresponding to phenomena monitored by human experts, it is possible to construct an intelligent machining system emulating the decision making of a human expert. This paper concentrates on defining the requirements for the inference part of such of an intelligent machining system. This work concentrates on both functional requirements, such as capability to take into account specific cutting states. The existence of process monitoring subsystems which detect and measure the cutting phenomena is assumed. As a result, a Sugeno-type fuzzy control is suggested, and feasibility and the level of completeness of such a system are discussed and issues requiring further study are identified.

Emerging Application of Fuzzy Expert System in Medical Domain

2015 ◽  
pp. 1-20 ◽  
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

Fuzzy Logic Based Approach for Power System Fault Section Analysis

Fuzzy Systems ◽  
2017 ◽  
pp. 987-1002
Author(s):  
Neeti Dugaya ◽  
Smita Shandilya
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Power System ◽  
Fuzzy Expert System ◽  
Circuit Breakers ◽  
Membership Grade ◽  
Protective Relays ◽  
Basic Network ◽  
The One ◽  
Maximum Selection

In this chapter, a fuzzy expert system is developed to assist the operators in fault detection. It requires much less memory to store the database (power system topology and the post fault status of circuit breakers and protective relays). The fuzzy expert system identifies two basic network section sets, Shealthy for the healthy sub network and Sisland for the fault islands, using the post fault status of circuit breakers and relays. It then calculates membership function for each possible fault section. The objective of this calculation is to determine the likelihood of each candidate fault section as the actual fault section. Moreover membership functions provide a convenient means of ranking among possible (or candidate) fault sections, and are the most important factors in decision making. During decision making, the most possible fault section is determined by maximum selection method. In this method most possible fault section is the one which is having highest membership grade. MATLAB code for the proposed scheme is developed and the results obtained in four cases for a power- system network.

Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology

Fuzzy Systems ◽  
2017 ◽  
pp. 418-442
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Fuzzy Rule ◽  
Fuzzy Expert System ◽  
Assessment Methodology ◽  
Correct Decision ◽  
Fuzzy Similarity ◽  
Computer Based ◽  
Fuzzy Assessment

In the field of medicine decision making it is very important to deal with uncertainties, knowledge, and information. Decision making depends upon the experience, capability, and the observation of doctors. In the case of complex situations, it is very tough to give a correct decision. So computer-based procedure is very much essential. Fuzzy Expert System is used for decision making in the field of medicine. Fuzzy expert system consists of the following elements, fuzzification interface, S Fuzzy Assessment Methodology, and defuzzification. S Fuzzy Assessment Methodology uses the K Ratio to find overlap between membership function. To measure the similarity between fuzzy set, fuzzy number, and fuzzy rule, T Fuzzy similarity is used. Similar fuzzy sets are merged to form a common set; a new methodology was framed to identify the similarity between fuzzy rules with fuzzy numbers, and S Weights are to manage uncertainty in rules. S Weights use consequent and antecedent part of each rule. The efficiency of the proposed algorithm was implemented using MATLAB Fuzzy Logic tool box to construct a fuzzy expert system to diagnose diabetes.

scholarly journals Implementation constraints and opportunities in developing expert system solutions for diagnosis of common diseases found in Taraba State

2018 ◽  
Vol 4 (3) ◽  
pp. 18
Author(s):  
Aliyu Sani Ahmad
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Clinical Observation ◽  
Fuzzy Expert System ◽  
Medical Field ◽  
Common Diseases ◽  
Information And Communication

Digital age has reform decision making especially in medical field through information and communication technology which become inevitable part of our lives. this paper illustrates the implementation constraint that encompasses developing Fuzzy Expert System (FES) for diagnosis of common diseases usually found in Taraba State. The paper, shows how fuzzy expert works through four distinct phases. It is discovered that the ratio of doctors to patients and the ratio of hospitals to doctors in Taraba is too low. Different literature that discussed how expert systems for diagnosing various diseases were reviewed; Interview, clinical observation, asking question and internet services were used as methodology for accomplishing this paper.  Result were illustrated and finally conclusion was drowned which shows that e-medical solution for diagnosing disease would do well in Taraba because of the opportunities it offers but it loaded with challenges and implementation constraint.

Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology

2015 ◽  
pp. 280-301
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Fuzzy Rule ◽  
Fuzzy Expert System ◽  
Assessment Methodology ◽  
Correct Decision ◽  
Fuzzy Similarity ◽  
Computer Based ◽  
Fuzzy Assessment

In the field of medicine decision making it is very important to deal with uncertainties, knowledge, and information. Decision making depends upon the experience, capability, and the observation of doctors. In the case of complex situations, it is very tough to give a correct decision. So computer-based procedure is very much essential. Fuzzy Expert System is used for decision making in the field of medicine. Fuzzy expert system consists of the following elements, fuzzification interface, S Fuzzy Assessment Methodology, and defuzzification. S Fuzzy Assessment Methodology uses the K Ratio to find overlap between membership function. To measure the similarity between fuzzy set, fuzzy number, and fuzzy rule, T Fuzzy similarity is used. Similar fuzzy sets are merged to form a common set; a new methodology was framed to identify the similarity between fuzzy rules with fuzzy numbers, and S Weights are to manage uncertainty in rules. S Weights use consequent and antecedent part of each rule. The efficiency of the proposed algorithm was implemented using MATLAB Fuzzy Logic tool box to construct a fuzzy expert system to diagnose diabetes.

Adaptable Fuzzy Expert System for Ship Lock Control Support

2016 ◽  
Vol 69 (6) ◽  
pp. 1341-1356 ◽  
Author(s):  
Todor Bačkalić ◽  
Vladimir Bugarski ◽  
Filip Kulić ◽  
Željko Kanović
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Specific Area ◽  
Fuzzy Expert System ◽  
Control Logic ◽  
Extensive Experience ◽  
Inference System ◽  
And Control ◽  
Ship Lock

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

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