• Computers and Concrete
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• 제21권1호
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• pp.21-30
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• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.129-140
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• 2017

This research was conducted in order to solve the problem of selecting an operational strategy for projects in project-based organizations by designing a fuzzy expert system. In the current research, we first determined the contributing parameters in operational strategy of project-based organizations based on existing research literature and experts' opinion. Next, we divided them into two groups of model inputs and outputs and the rules governing them were determined by referring to research literature and educational instances. In order to integrate rules, the revised Ternary Grid (revised TG) and expert opinions were applied according to a hybrid algorithm. The Ultimate rules were provided in Fuzzy Inference System format (FIS). In this FIS, proper manufacturing decisions are recommended to the user based on selected competitive priority and also project properties. This paper is the first study in which rules and relations governing the parameters contributing operational strategy in project-based organizations are acquired in a guided integrated process and in the shape of an expert system. Using the decision support system presented in this research, managers of project-based organizations can easily become informed of proper manufacturing decisions in proportion with selected competitive priority and project properties; and also be ensured that theoretical background and past experiences are considered.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.140-145
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• 2008

In this paper, we constructed the advanced cargo monitoring system for liquid cargo tankers which embedded the Decision Support System (DSS) based on the International Ship Management Code (ISM Code). To make this system, we first organized a base of expert's knowledge concerning liquid tanker operations that largely affect ocean accidents. We can find out the knowledge via inference method which simply imitates the fuzzy inference method. Based on this expert's knowledge, we constructed the DSS that provides a code of conduct for operating cargo tanks safely. The proposed monitoring system could eliminate human error when confronting dangerous situations, so the system will help sailors to operate cargo tanks safely.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.249-252
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• 1996

Fuzzy logic has been successfully used for nonlinear control systems. However, when the plant is complex or expert knowledge is not available, it is difficult to construct the rule bases of fuzzy systems. In this paper, we propose a new method of how to construct automatically the rule bases using fuzzy neural network. Whereas the conventional methods need the training data representing input-output relationship, the proposed algorithm utilizes the gradient of the object function for the construction of fuzzy rules and the tuning of membership functions. Experimental results with the inverted pendulum show the superiority of the proposed method in comparison to the conventional fuzzy controller.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1808-1811
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• 1997

A position control algorithm for a flexible manipulato is stuudied. The proposed algorithm is based on a fuzzy theroy with a Steady State Genetic Algorithm(SSGA). The conventional fuzzy methods need expert's knowledges or human experiences. The SSGA, which is one of the optimization algorithms, tunes automatically the input-output membership parameters and fuzzy rules. The computer simulation is presented ot illustrate the approaches. Finally we applied a fuzzy theory with a SSGA to aposition control of a flexible manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.182-182
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• 2000

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion. The trajectory trucking experiment of the polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding model controller provides reliable tracking performance during the polishing process.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.156-156
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• 2000

In this paper, a new method for colour measuring is presented using fuzzy modeling technique. The fuzzy and polynomial inferences are used for obtaining RGB characteristic curve. The eight RGB real data from expert dye-stuff manufacturer, are simulated. The results show that the proposed method will is more excellent than other methods, in the colour measuring process of textile field.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.183-186
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• 2000

In this paper, a new method for colour measuring is presented using fuzzy modeling technique. The fuzzy and polynomial inferences are used for obtaining RGB characteristic curve. The eight RGB real data from expert dye-stuff manufacturer, are simulated. The results show that the proposed method will is more excellent than other methods, In the colour measuring process of textile field.

• 한국지능시스템학회논문지
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• 제2권1호
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• pp.33-52
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• 1992

In this paper, Controlled Knowledge Base(CKB) type fuzzy learning controller for force/servo control system was proposed and the application for them was also studied. To achieve them, we derive fuzzy set from expert knowledges and reson the appropriate control gains by parameter estimation of object. Then, we proved it by computer simulation that we can reduce the ambigious effect, which is not able to be estimated, by designing the controller based on CKB.