Penerapan Fuzzy Inference System Mamdani Untuk Mengukur Tingkat Kenyamanan Pengunjung

This research is intended to determine the comfort level of visitors in order to improve comfort quality by knowing prioritized attributes to be maintained and improved by Mekarsari Fruit Garden Bogor. The method used is Fuzzy Inference System using max-min method who have membership degree min 0 and max 1 and followed by defuzzification. There are three criterias used that is variable service, facilities and cleanliness. Each variable has an indicators and the indicators used is 13 includes five questions on the performance variables, five questions on the variable facilities and 3 questions on cleanliness variables. Based on defuzzification obtained sample that categorized give good ratings to the convenience of visitors with score 9.5 and include good category

Fuzzy Inference System Untuk Menentukan Tingkat Kompetensi Kepribadian Guru

Teacher\u27s role is very important in school because they who will give the lessons to their students. Therefore, ateacherwhowillteachin theschools shouldhave the competencies, especially in personality competency. This research is belonged to Fuzzy Inference System with Tsukamoto method for determining the competency based on the teacher\u27s personality with a web base. In Tsukamoto method each consequent to the rules in the form of IF-THEN should be represented by a set of fuzzy with monotone membership function. The output inference of each rule is given explicitly (crisp) based on α-predicate (fire strength). The end result is obtained by using weighted average.Fuzzy data used is score data or value of personality competence. The rating score with scale A means (very good), B means (fair), C means (less good). Each personality competence component mentioned is used as figuration rule data by producing 243 rules. These rules are used to find µ-score in each variable that has been determined to find z scor

An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system based approach to estimate prices for residential properties. The data set consists of historic sales of homes in a market in Midwest USA and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

Advanced inference in fuzzy systems by rule base compression

This paper describes a method for rule base compression of fuzzy systems. The method compresses a fuzzy system with an arbitrarily large number of rules into a smaller fuzzy system by removing the redundancy in the fuzzy rule base. As a result of this compression, the number of on-line operations during the fuzzy inference process is significantly reduced without compromising the solution. This rule base compression method outperforms significantly other known methods for fuzzy rule base reduction.Peer Reviewe

Fuzzy Inference System for fault detection in internal combustion engines in Thermoelectric Power Generating Plants

In this work, an approach to implement a simplified fuzzy inference model for monitoring the conditions of workings of power generators through the pressure values ​​of combustion temperature and engine water pressure is displayed. The model helps the supervisory system, through real-time evaluation of the operating conditions of the engine in percentage rates. The application of tools based on computational intelligence, have shown efficiency in various areas of industrial engineering

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches