Penerapan Konsep Fuzzy Dalam Variable-centered Intelligent Rule System (Studi Kasus: Pemilihan Jurusan Di Chinese University of Hongkong)

Variable-Centered Intelligent Rule System (VCIRS) is a system which is inspired by Rule-based System (RBS) and Ripple Down Rules (RDR). The system architecture is adapted from RBS, while from RDR this system obtained its advantages. The system organized Rule Base (RB) in a special structure so that easy knowledge building, powerful knowledge inferencing and evolutionally system performance refining can be obtained in the same time. In this paper, the architecture of VCIRS is used to build an expert system for helping students to choose a department at a university. The application of this expert system is able to handle fuzzy concepts (e.g., such as good, high or rather high) which is a prominent part of sentences in natural language. This system is able to cope with exact values, fuzzy (or inexact) values and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts. An application example in this paper is a RBS which is employed fuzzy logic and fuzzy number for inexact reasoning. It uses two inexact basic concepts, i.e., fuzziness and uncertainty. A case study presented here is the department admission at Chinese University of Hongkong, formed in a RB containing with fuzzy and normal terms. From experiments performed, there's the proper result obtained comparing with the result from Z-II system (i.e., a comprehensive expert system builder tool developed by Chinese University of Hongkong) which is this paper refers to. So that the conclusion is a fuzzy VCIRS proposed here, is working properly and producing the right and true results

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Modelling the Shifts in Activity Centres along the Subway Stations. The Case Study of Metropolitan Tehran

Activity centers are areas of strong development of a particular activity, such as residence, employment or services. Understanding the subway system impacts on the type, combination, distribution and the development of basic activities in such centers plays an important role in managing development opportunities created along the Tehran subway lines. The multi criteria and fuzzy nature of evaluating the development of activity centers makes the issue so complex that it cannot be addressed with conventional logical systems. One of the most important methods of multi criteria evaluation is Fuzzy Inference System. Fuzzy inference system is a popular computing framework based on the concepts of Fuzzy Sets Theory, which is capable of accommodating inherent uncertainty in the multi-criteria evaluation process. This paper analyses shifts in activity centers along two lines of the Tehran subway system based on three major criteria by designing a comprehensive fuzzy inference system. The data for the present study were collected through documentary analysis, questionnaires and semi-structured interviews. The result revealed that the level of the subway system influence on the pattern and process of the development of activities varied with the location, physical environment and entity of each station. Furthermore, empirical findings indicated that the subway line might weaken residential activities while attracting employment and service activities to the city center. Specifically, residential estates have moved away from the city center to the suburbs whereas employment and service activities have expanded from the existing central business district (CBD). The results can be applied to suggest planning policies aimed at improving the effects of public transit on property development and land use change in a developing country

A fuzzy knowledge based system for clinical diagnosis of tropical fever

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sıtma ve tifo Sahra-altı Afrika'nın en büyük tropikal ateş enfeksiyonlarıdır. Her ikisi de bölgenin hastalık, ölüm ve ekonomik kayıplarının sebebidir. Tifo ateşi sebebiyle, her 100.000 kişiden 725 tifo vakasına yakalanmakta ve bu hastalardan da 7 adedi ölümle sonuçlandığı tahmin edilmektedir ve Dünya'nın sıtma ölümlerinin %90'ı Sahra-altı Afrika'da meydana gelmektedir. Bu iki hastalığın teşhisinde önemli olan çok sayıda belirti bulunması ve birçoğunun da ortak olması dolayısıyla teşhis zorlaşmaktadır. Bulanık küme teorisine ve insan gibi sonuçlandırma üzerine dayanan bulunak mantık, insani bilimlerde yaygın olarak kullanılmakta ve birçok problemi başarılı bir şekilde çözmektedir. Sınıflandırma ve karar verme görevlerine ihtiyaç duyulan tıbbi teşhis bu cazip uygulamalardan biridir. Belirsizliklerin olduğu teşhis özelliklerindeki karmaşıklıklar bilgisayar sistemlerinde kullanılan doğal dil ile üstesinden gelinmiştir. Bu çalışmada, Sahra-altı Afrika'da sıtma ve tifo ateşinin klinik teşhisi için bilgi tabanlı teşhis sisteminin (TROPFEV) tasarımında bulanık mantık kullanımı anlatılmaktadır. Bilgiler, tıp uzmanları danışmanlığında Uganda Sağlıklı Bakanlığı tarafından hazırlanan UCG-2012'den (Uganda Klinik Klavuzu 2012) çıkarım yapılmıştır. Bu kaynaklardan edinilmiş bilgiler modellenip, bulanık kural tabanlı mantık kullanılarak tanımlanmış ve Matlab 2012a gerçeklenmiştir. Toplanan bilgilere göre, 21 adet teşhis özellikleri, ateş hastalığının durumuna ya da şiddetine göre sistemi oluşturmak için düzenlenmiştir. Kullanıcı, karmaşık-sıtma, karmaşık olmayan-sıtma, karmaşık-tifo, karmaşık olmayan-tifo veya bilinmeyen ateş cevabını sistemden beklemektedir. Test ve performansını değerlendirmek için, TROPFEV sistemin sonuçları ile doktor tarafından yapılan teşhis sonuçlarıyla karşılaştırılmıştır. Uzman teşhisleri ve sistem teşhisleri arasındaki % 86 oranında doğruluk olduğunu görülmüştür. Sonuç olarak, tıbbi teşhis için tecrübesiz hekimlerin teşhislerine daha hızlı ve verimli bir şekilde teşhis koyabilmek için yardımcı olması amacıyla bulanık mantık kullanımına ağırlık verilebilir.. Çünkü bulanık mantık belirtilerdeki kesin olmama sıkıntılarının üstesinden gelebilmek için bulanıklık kümelerini kullanır ve bir sınıflandırmaya ilişkilendirir.Malaria and typhoid fever are major tropical fever infections. Both are responsible for significant morbidity, mortality and economic loss in the region. Typhoid fever is estimated to cause 725 incident cases and 7 deaths per 100,000 people in the year and on the other side 90% of the total world malaria deaths occur in the Sub-Saharan Africa. The two diseases malaria and typhoid fever have several diagnosis features with overlapping signs and symptoms which are a task in medical diagnosis. Fuzzy logic that lies on the fuzzy set theory and similar to human reasoning is widely used for human-related sciences, and successfully solves many problems. Medical diagnosis is one of these attractive applications, which requires classification and decision making tasks. It uses natural language to represent data into computer systems where complications in diagnosis features such as vagueness are perfectly handled. This thesis describes the use of fuzzy logic to design a knowledge based system for clinical diagnosis of malaria and typhoid fever (TROPFEV) in Sub-Saharan Africa. Knowledge was extracted from the documentary of UCG-2012 (Uganda Clinical Guidelines 2012) prepared by the ministry of healthy in Uganda as well as consulting medical experts. The knowledge acquired from these resources is modelled, represented using fuzzy rule based reasoning and implemented in Matlab 2012 a. According to the collected knowledge, 21 diagnosis features have been organised with their situations or severity during fever infections to build the system. The user is expected to get the answer of complicated malaria, uncomplicated malaria, complicated typhoid, uncomplicated typhoid or unknown fever. For testing and evaluating its performance, the results of the TROPFEV system were compared with the results of diagnosis made by a real doctor The difference in results between expert diagnosis and system diagnosis showed that the expert system have similarity with the real experts with 86% accuracy. In conclusion, the use of fuzzy logic in medical diagnosis can be emphasized because it provides an efficient way to assist inexperienced physicians to arrive at the final diagnosis of fever more quickly and efficiently. This is because fuzzy logic applies fuzzy sets to handle vagueness existing in symptoms

Automated construction of fuzzy event sets and its application to active databases

Fuzzy sets and fuzzy logic research aims to bridge the gap between the crisp world of math and the real world. Fuzzy set theory was applied to many different areas, from control to databases. Sometimes the number of events in an event-driven system may become very high and unmanageable. Therefore, it is very useful to organize the events into fuzzy event sets also introducing the benefits of the fuzzy set theory. All the events that have occurred in a system can be stored in event histories which contain precious hidden information. In this paper, we propose a method for automated construction of fuzzy event sets out of event histories via data mining techniques. The useful information hidden in the event history is extracted into a matrix called sequential proximity matrix. This matrix shows the proximities of events and it is used for fuzzy rule execution via similarity based event detection and construction of fuzzy event sets. Our application platform is active databases. We describe how fuzzy event sets can be exploited for similarity based event detection and fuzzy rule execution in active database systems

A Study on the Error Compensation of Image Processing Control System using Artificial Intelligence

It remains simple automation on processing of the work which manufactures raw materials in a primary industry and demands much more manpower than the others category of industry. It has developed and applied automatic machine due to reduce processing line and downsize manpower per processing line, But the operators waste a lot of time to revise size and assort the size of object when the machine is operating. The subject is the fish head cutter which is impossible to efficient operating and inevitable stop without assort of fish size before it's operating and the processing system which is basically composed of pattern and distinguishing feature of the object and obtain to high quality object image in real time using with CCD(Charge Coupled Device) camera without the limitation and assortment of the size and especially, the important of this paper is to acquire optimum image when established controller attach on processing system and to compensate error between coordinates on the acquired image and projection on the coordinates of real processing space. The restoration optimum image from transformation image demands compound change of image change, calendar reform, and multiple soultion calculation. This study presents that system actively deal with outside interference, vibration, movement, mechanical feature on the operating or before the operating and avoids complicated mathematical numerical expression and revise based on expert knowledge based which applies Fuzzy Logic which is one of Artificial Intelligent technique.목차 제 1 장 서론 = 1 제 2 장 퍼지이론 = 3 2.1 퍼지이론의 개요 = 3 2.2 퍼지추론 = 5 2.3 다변수 구조 퍼지시스템 = 9 제 3 장 퍼지논리를 이용한 오차보정 = 12 3.1 영상의 기본적 변환 = 12 3.2 퍼지논리에 의한 에러 보정 = 21 3.3 영상에서의 퍼지입력 파라메타의 산출 = 22 3.4 보정계수 산출을 위한 퍼지추론 = 27 제 4 장 시스템의 구현 및 실험 = 30 4.1 시스템 구성과 개요 = 30 4.2 전체 가공기의 동작 시？스 = 32 4.3 실험결과 = 35 제 5 장 결론 = 39 참고문헌 = 4

General purpose, client configurable fuzzy logic expert control system

In this paper we describe a fuzzy logic based control system implemented on a PC architecture. The rule based inference engine of this expert system is easily configured through an ascii text file and is demonstrated to be capable of controlling various simulations, including an inverted pendulum. The controller is designed to be of general purpose use and is client configurable. We will also briefly discuss the simulations themselves and then examine the object oriented design of the system and the benefits it confers

Sistema especialista difuso para controle de estações de tratamento de esgotos pelo processo de lodos ativados

Dissertação (mestrado) - Universidade Federal de Santa Catarina. Centro TecnologicoEste trabalho apresenta como objetivo geral o desenvolvimento de um sistema especialista difuso para controle de estações de tratamento de esgotos pelo processo de lodos ativados. Os responsáveis pelo controle operacional dessas estações, encontram sérias dificuldades. Em vista disso, torna-se necessário a pesquisa e o desenvolvimento de novos métodos, com o intuito de descrever o mais corretamente possível a situação da operação. Inicialmente é feito um estudo das técnicas de inteligência artificial, da teoria do conjuntos difusos e do tratamento de esgotos, especificamente, pelo processo de lodos ativados. A seguir, é construído um modelo baseado em lógica difusa, no qual se determinam as relações difusas entre as variáveis de estado e de controle. Essas relações são implemantadas num base de conhecimento. Finalmente, é feita uma aplicação prática do modelo e sua implementação computacional, sendo verificada sua operacionalidade e avaliados os resultados obtidos