KNOWLEDGE EXTRACTION FROM FALL-TIME AUTO-CORRELATED PATTERNS BY USING NEURAL RULE BASED EXPERT SYSTEM

2006 ◽  
Vol 03 (01) ◽  
pp. 15-24 ◽  
Author(s):  
NAZAR ELFADIL ◽  
INTISAR IBRAHIM
Keyword(s):  
Expert System ◽  
Correlated Data ◽  
Self Organizing Maps ◽  
Data Set ◽  
Rule Based ◽  
Fall Time ◽  
Knowledge Based ◽  
Correlated Patterns ◽  
Automated Knowledge ◽  
Learning Data

In this paper, the author presents an approach for automated knowledge acquisition system using Kohonen self-organizing maps and k-means clustering. The extracted knowledge in terms of rules are used as knowledge base for a rule based expert system. For the sake of illustrating and validating the system overall architecture, a fall-time auto-correlated data patterns has been used as a learning data set. The verification of the produced knowledge based was conducted by conventional expert system.

KNOWLEDGE EXTRACTION FROM RISE-TIME AUTO-CORRELATED PATTERNS

2008 ◽  
Vol 05 (02) ◽  
pp. 181-187 ◽  
Author(s):  
NAZAR ELFADIL
Keyword(s):  
Expert System ◽  
Rise Time ◽  
Knowledge Extraction ◽  
Correlated Data ◽  
Self Organizing Maps ◽  
Data Set ◽  
Knowledge Based ◽  
Correlated Patterns ◽  
Automated Knowledge ◽  
Learning Data

In this paper, the author presents an approach for automated knowledge extraction from rise time auto-correlated patterns by using self-organizing maps and k-means clustering. The extracted knowledge in terms of rules will be used as knowledge base for an expert system. Rise-time auto-correlated data patterns are used as a learning data set. The produced knowledge based was verified by using a conventional expert system.

Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

2005 ◽  
Vol 9 (6) ◽  
pp. 693-697 ◽  
Author(s):  
Nazar Elfadil ◽  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Training Data ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Data Set ◽  
Unsupervised Neural Network ◽  
Automated Knowledge ◽  
Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

scholarly journals Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM

2013 ◽  
Vol 7 (1) ◽  
pp. 45
Author(s):  
Nur Hasanah ◽  
Retantyo Wardoyo
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Inference Engine ◽  
Rule Based ◽  
Knowledge Based ◽  
Menu Planning ◽  
Model Complex ◽  
Uncertainty Method ◽  
Management Expert ◽  
Diabetes Melitus

AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product.            Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu  AbstractIt is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average.            The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu

ICOT: An Integrated C-Object Tool for Knowledge-Based Programming

1998 ◽  
Vol 06 (03) ◽  
pp. 273-293 ◽  
Author(s):  
Hyung Jeong Yang ◽  
Jae Dong Yang ◽  
Yeongho Kim
Keyword(s):  
Expert System ◽  
Object Oriented ◽  
Fuzzy Information ◽  
Major Drawback ◽  
Rule Based ◽  
Knowledge Based ◽  
Complex Engineering ◽  
Knowledge Based Engineering ◽  
Current Rule ◽  
New Framework

In this paper, an Integrated C-Object Tool, namely ICOT, is proposed for knowledge-based programming. A major drawback of current rule-based expert system languages is that they have difficulty in handling composite objects as a unit of inference. An object-oriented model is a powerful alternative to complement the drawback. Each of these alone cannot capture all the semantics of knowledge, particularly in complex engineering domains. For a knowledge-based approach to be effective, both the object-oriented paradigm and the rule-based mechanism may need to be integrated into one framework. The framework may also need to support manipulation of fuzzy knowledge to model the real world as close as possible. Three types of fuzzy information are identified, and a proper way of representing and inferencing them is developed. ICOT provides a new framework into which rule-based deduction, object-oriented modeling, and fuzzy inferencing are combined altogether. This can become especially useful for developing knowledge-based engineering applications.

scholarly journals Rule Based and Supervisory Training Approach To Develop Expert System Tool For Detecting Lung Cancer Disease

2010 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
K. Balachandran ◽  
R. Anitha
Keyword(s):  
Lung Cancer ◽  
Expert System ◽  
Expert Systems ◽  
Medical Attention ◽  
Cancer Disease ◽  
Rule Based ◽  
Knowledge Based ◽  
Training Approach ◽  
Dreaded Disease ◽  
Rule Based Approach

Knowledge-based expert systems, or expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Lung cancer is one of the dreaded disease in the modern era. It is responsible for the most cancer deaths in both men and women throughout the world. Early diagnosis and timely treatment are imperative for the cure. Longevity and cure depends on early detection. This paper gives on insight to identify the forget group of people who are suffering or susceptible to suffer lung cancer disease. Seeking proper medical attention con be initiated based on the findings. Expert system tool developed, to find this target group based on the non-clinical parameters. Symptoms and risk factors associated with Lung cancer ore token as the basis of this study. This expert system basically works on the rule based approach to collect the data. Then Supervisory learning approach is used to infer the basic data. Once sufficient knowledge base is generated the system can be made to adopt in unsupervised learning mode.

scholarly journals A methodology for designing electrification programs for remote areas

2019 ◽  
Vol 28 (4) ◽  
pp. 1265-1290 ◽  
Author(s):  
Javier León ◽  
F. Javier Martín-Campo ◽  
M. Teresa Ortuño ◽  
Begoña Vitoriano ◽  
Luis Miguel Carrasco ◽  
...  
Keyword(s):  
Expert System ◽  
Cost Estimation ◽  
Programming Model ◽  
Spare Parts ◽  
Mixed Integer ◽  
Optimal Size ◽  
Data Set ◽  
Rule Based ◽  
Remote Areas ◽  
Maintenance Systems

Abstract One of the UN Sustainable Development Goals is the supply of sustainable energy even where no electrical grid is available. The photovoltaic rural electrification programs are the most common systems implemented in remote areas, especially in developing countries. These programs include the systems installation and their maintenance for a given period. Installation costs and even spare parts costs over time are usually well estimated. However, design and cost estimation of the maintenance systems is a difficult task, whose wrong management has often resulted in the failure of these electrification programs. In this work, a methodology for designing maintenance systems and estimating costs is presented. The methodology includes a mixed integer linear programming model and a rule based expert system. The mathematical programming model allows obtaining the optimal size and accurate cost estimation of a maintenance system, based on precise information about the installed systems. This model is calibrated and validated with real running programs and will be used to get an enlarged data set of simulated cases if needed. The rule based expert system is obtained from the data set applying classification and regression methods with general information about the region and program to be run. It can be used for designing programs or for companies making decisions about being involved in a program to be developed. The methodology has been applied to real Morocco programs.

Expert Systems in Electrical Engineering Education

1988 ◽  
Vol 25 (2) ◽  
pp. 113-124 ◽  
Author(s):  
H. E. Hanrahan
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Electrical Engineering ◽  
Bachelor's Degree ◽  
Engineering Practice ◽  
Rule Based ◽  
Knowledge Based ◽  
The Status ◽  
Electrical Engineering Education ◽  
Future Potential

This paper reviews the status and future potential of knowledge-based expert systems in relation to electrical engineering practice and education. A generalised rule-based expert system is described. Uses of expert systems in the Bachelor's Degree are identified by means of examples. Software and tools are discussed.

scholarly journals Rule Based and Supervisory Training Approach to Develop Expert System Tool for Detecting Lung Cancer Disease

2010 ◽  
Vol 9 (2) ◽  
pp. 62-71
Author(s):  
K. Balachandran ◽  
R. Anitha
Keyword(s):  
Lung Cancer ◽  
Expert System ◽  
Expert Systems ◽  
Target Group ◽  
Medical Attention ◽  
Cancer Disease ◽  
Rule Based ◽  
Knowledge Based ◽  
Dreaded Disease ◽  
Rule Based Approach

Knowledge-based expert systems, or expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Lung cancer is one of the dreaded disease in the modern era. It is responsible for the most cancer deaths in both men and women throughout the world. Early diagnosis and timely treatment are imperative for the cure. Longevity and cure depend on early detection. This paper gives on insight to identify the target group of people who are suffering or susceptible to suffer lung cancer disease. Seeking proper medical attention can be initiated based on the findings. Expert system tool developed, to find this target group based on the non-clinical parameters. Symptoms and risk factors associated with Lung cancer are taken as the basis of this study. This expert system basically works on the rule based approach to collect the data. Then Supervisory learning approach is used to infer the basic data. Once sufficient knowledge base is generated the system can be mode to adopt in unsupervised learning mode.

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