scholarly journals Prediction of Rainfall Characteristics Based on Fuzzy Expert System

2020 ◽  
Vol 8 (5) ◽  
pp. 1125-1130
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Tamil Nadu ◽  
Weather Prediction ◽  
Research Work ◽  
Fuzzy Rules ◽  
Dew Point ◽  
Modern World ◽  
Fuzzy Expert System ◽  
Rainfall Prediction

Rainfall prediction is one of the most extremely important and trickiest job in the modern world because, Rain is the lifeblood of human survival, of life on our planet. Gorgeous beauty of fuzzy logic was described by various author for prediction in various field. In this work, fuzzy logic is applied in proposing a model to predict rainfall percentage with parameters. Fuzzy Expert System inputs(parameter) include the temperature, humidity, wind speed , Dew point etc., with Output as rainfall Percentage .Here we develop a new model as RPFES Model to predict rainfall percentage for particular geographical location of Tamil Nadu. There are four steps for developing RPFES Model : The first step is Fuzzification process by triangular membership function for representing the input variables and output variables. The next step is Fuzzy Inference with Fuzzy Rules the method applied in this research work are Root Sum Square (RSS). The Root Sum Square of drawing inference was employed to infer the data from the fuzzy rules developed and finally we move to Defuzzification process for getting Rainfall percentage by individually. From RPFES Model, Individual Person get the Rainfall Prediction as Percentage with purely by Fuzzy Logic and not by Metrological Center. Weather Prediction is the major essential and challenging operational responsibilities which are carried out by meteorological services all over the world.

Qualitative Evaluation of Engineering Designs Using Fuzzy Logic

1996 ◽  
Author(s):  
Rajkumar Roy ◽  
Ian C. Parmee ◽  
Graham Purchase
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Engineering Design ◽  
Evaluation System ◽  
Design Space ◽  
Qualitative Evaluation ◽  
Fuzzy Rules ◽  
Design Solution ◽  
Fuzzy Expert System ◽  
Adaptive Search

Abstract The paper describes a Qualitative Evaluation System developed using a fuzzy expert system. The evaluation system gives a qualitative rating to design solutions by considering manufacturability aspects, choice of materials and some special preferences. The information is used in decision support for engineering design. The system is an integrated part of a decision support tool for engineering design called the ‘Adaptive Search Manager’ (ASM). ASM uses an adaptive search technique to identify multiple design solutions for a 12 dimensional Turbine Blade Cooling System design problem. Thus the task has been to develop a fuzzy expert system that can qualitatively evaluate any design solution from a design space using a realistically small number of fuzzy rules. The developed system utilises a knowledge separation and then a knowledge integration technique. The design knowledge is first separated into three categories: inter variable knowledge, intra variable knowledge and heuristics. Inter variable knowledge and intra variable knowledge are integrated using a concept of “compromise”. The qualitative evaluation system can evaluate any design solution within the 12 dimensional design space, but uses only 44 fuzzy rules and one function that implements the inter variable knowledge.

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.

A FUZZY EXPERT SYSTEM BASED ON POSITIVE RULES FOR DEPRESSION DIAGNOISIS

2020 ◽  
pp. 33-39
Author(s):  
Mai Thi Nu
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Knowledge Base ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Medical Records ◽  
Intelligent Systems ◽  
Fuzzy Expert System ◽  
Test Results

Fuzzy set theory and fuzzy logic are highly suitable mathematical tools for developing intelligent systems in medicine. This paper presents a fuzzy expert system based on positive rules for diagnosing depression types. A knowledge base that includes more than 800 positive rules to determine diagnostic conclusions for 04 types of depression. The expert system has been tested on more than 200 medical records of depressed patients. Test results show the suitable accuracy of the system in diagnosis.

scholarly journals Fuzzy Expert System for Severity Measurement of COVID-19 Suspect

2021 ◽  
Vol 8 (12) ◽  
pp. 139-144
Author(s):  
B.T. Jadhav ◽  
G.S. Nhivekar
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Severity Score ◽  
Severe Illness ◽  
Fuzzy Expert System ◽  
The World ◽  
Fuzzy Logic Expert System ◽  
High Uncertainty

The pandemic of COVID-19 disease is spread over the world. The symptoms of COVID-19 disease can vary from mild to severe illness. Also, these symptoms are complex and uncertain in nature. The severity score is useful to treat the suspect that highly depends on symptoms. To handle with this problem, the current study makes use of the Fuzzy Expert System which is one in every of the foremost suitable methods in modelling systems with high uncertainty and complexity. In this study, the fuzzy-based expert system is designed to measure the severity of COVID-19 disease in suspect. Keywords: Fuzzy Logic, Expert System, COVID-19 .

scholarly journals Characterization of Human Gait using Fuzzy Logic

10.14311/1789 ◽  
2013 ◽  
Vol 53 (2) ◽  
Author(s):  
Patrik Kutilek ◽  
Slavka Viteckova ◽  
Zdenek Svoboda
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Inclination Angle ◽  
Walking Speed ◽  
Fuzzy Logic System ◽  
Human Walking ◽  
Human Gait ◽  
Fuzzy Expert System ◽  
Movement Type ◽  
Logic System

In medical practice, there is no appropriate widely-used application of a system based on fuzzy logic for identifying the lower limb movement type or type of walking. The object of our study was to determine characteristics of the cyclogram to identify the gait behavior by using a fuzzy logic system. The set of data for setting and testing the fuzzy logic system was measured on 10 volunteers recruited from healthy students of the Czech Technical University in Prague. The human walking speed was defined by the treadmill speed, and the inclination angle of the surface was defined by the treadmill and terrain slope. The input to the fuzzy expert system is based on the following variables: the area and the inclination angle of the cyclogram. The output variables from the fuzzy expert system are: the inclination angle of the surface, and the walking speed. We also tested the method with input based on the angle of inclination of the surface and the walking speed, and with the output based on the area and the inclination angle of the cyclogram. We found that identifying the type of terrain and walking speed on the basis of an evaluation of the cyclogram could be sufficiently accurate and suitable if we need to know the approximate type of walking and the approximate inclination angle of the surface. According to the method described here, the cyclograms could provide information about human walking, and we can infer the walking speed and the angle of inclination of the terrain.

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.

Fuzzy Expert System Development for Electric Generator Failure Diagnostics

2008 ◽  
Author(s):  
Shuzhen Xu ◽  
Enrique Susemihl
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Failure Mode ◽  
System Development ◽  
Fuzzy Expert System ◽  
Limited Time ◽  
Electric Generator ◽  
Amount Of Information ◽  
Turbine Generators ◽  
Qualitative Variables

This paper presents the development of an expert system to help engineers assess the condition of turbine generators. The expert system is designed to minimize the search and input of data in order to address the issue of the limited time available to engineers compared to the large amount of information to be investigated. To imitate the imprecise reasoning process as well as the vague information and qualitative variables inherent in this type of evaluation, fuzzy logic is used. An example of application to one failure mode, i.e., loose stator bar, is also presented.

scholarly journals Rain prediction using fuzzy rule based system in North-West malaysia

2019 ◽  
Vol 14 (3) ◽  
pp. 1564
Author(s):  
Noor Zuraidin Mohd Safar ◽  
Azizul Azhar Ramli ◽  
Hirulnizam Mahdin ◽  
David Ndzi ◽  
Ku Muhammad Naim Ku Khalif
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Complex Problem ◽  
Dew Point ◽  
Inference System ◽  
North West ◽  
Rainfall Prediction

<span>The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed.   Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%</span>

scholarly journals PENERAPAN FUZZY EXPERT SYSTEM UNTUK DIAGNOSA PENYAKIT TELINGA, HIDUNG DAN TENGGOROKAN BERBASIS ANDROID

2020 ◽  
Vol 4 (2) ◽  
pp. 57
Author(s):  
Mansuri Mansuri ◽  
Rury Retno Kartika
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Fuzzy Expert System

Meningkatnya perkembangan teknologi saat ini, salah satunya teknologi mobile atau smartphone, hal ini tidak bisa dipungkiri bahwa kehidupan manusia dengan perangkat mobile sudah melekat. Dengan adanya sistem operasi dalam smartphone, salah satunya sistem operasi android yang digunakan oleh teknologi mobile yang dapat membantu pekerjaan manusia Penelitian ini bertujuan untuk merancang aplikasi sistem pakar diagnose penyakit THT berbasis android dengan metode Fuzzy Logic. Dengan aplikasi ini dapat memeberikan informasi lebih cepat untuk mengetahui maslah yang terjadi pada bagian telinga, hidung, dan tenggorokan. Aplikasi ini juga dapat memberikan solusi yang terbaik dalam menentukan penanganan sesuai dengan diagnosa yang telah diketahui. Dengan adanya aplikasi ini pengguna layaknya seperti berhadapan dengan dokter langsung, jadi pengguna bisa mendapatkan penanganan yang lebih cepat tanpa harus memakan waktu untuk menemui dokter

Intelligent Medical Diagnostic System for Diabetes

2019 ◽  
pp. 188-209
Author(s):  
Jimmy Singla
Keyword(s):  
Expert System ◽  
Research Work ◽  
Diagnostic System ◽  
Fuzzy Expert System ◽  
Membership Functions ◽  
Neuro Fuzzy ◽  
Different Types ◽  
Medical Diagnostic ◽  
Sensitivity Specificity ◽  
The Comparative Study

In this chapter, the neuro-fuzzy technique has been used for the diagnosis of different types of diabetes. It has been reported in the literature that triangular membership functions have been deployed for Mamdani and Sugeno fuzzy expert systems that have been used for diagnosis of different types of diabetes. The Gaussian membership functions are expected to give better results. In this context, Gaussian membership functions have been attempted in the neuro-fuzzy system for the diagnosis of different types of diabetes in the research work, and improved results have been obtained in terms of different parameters like sensitivity, specificity, accuracy, precision. Further, for the comparative study, the dataset used for neuro-fuzzy expert system developed in this research work has been considered on Mamdani fuzzy expert system as well as Sugeno fuzzy expert system, and it has been confirmed that the result parameters show better values in the proposed model.

Sign in / Sign up

Export Citation Format

Share Document