• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.927-935
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• 2005

The certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions can be represented by intervals, such as vague numbers between zero and one based on vague sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner[18]. we are also proposed an efficient algorithm to perform vague set reasoning automatically. This vague set reasoning algorithm allows the rule-based systems to perform reasoning in a more flexible and more efficient.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1E
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• pp.64-74
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• 1997

In many decision making systems, rule-based approaches are used to solve complex problems in the areas of pattern analysis and computer vision. In this paper, we present methods for generating fuzzy IF-THEN rules automatically from training data for pattern classification and high-level computer vision. The rules are generated by construction minimal approximate fuzzy aggregation networks and then training the networks using gradient descent methods. The training data that represent features are treated as linguistic variables that appear in the antecedent clauses of the rules. Methods to generate the corresponding linguistic labels(values) and their membership functions are presented. In addition, an inference procedure is employed to deduce conclusions from information presented to our rule-base. Two experimental results involving synthetic and real are given.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.159-170
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• 1996

In this paper, we propose a generating method for the optimal rules for the nonlinear control system using evolution algorithms and fuzzy controller. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and. knowledge. and ran be intelligent control. The approachpresented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which Is tile defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method In non -linear systems.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.498-500
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• 1993

This paper presents handover algorithm method using fuzzy rules and neura1 network. In future mobile communication systems, the amount of call requests over a region will increase dramatically. This problem has to be solved by decreasing the cell size. But, this method lets a mobile station switch the a base station at a higher rate. In order to maintain better mobile communication system in a micro or pico cellular system, better handover algorithm must be devoloped. In this paper, we propose a handover algorithm which is based on the fuzzy teory that is applied to make rules with the parameters and neural network that is to learn rules. This new handover algorithm is tested by computer simulation and compared with the conventional algorithms.

• Journal of Ocean Engineering and Technology
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• v.21 no.6
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• pp.115-119
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• 2007

Inference rules of the knowledge base, generated by experts or optimization, may be often inconsistent and incomplete. This paper suggests a systematic and automatic method which extracts inference rules not from experts' subject but from data. First, input/output linguistic variables are partitioned into several properties by the fuzzy equalization algorithm and each combination of their properties comes to premise of inference rule. Then, the conclusion which is the mast suitable for the premise is selected by evaluating consistent measure. This method, automatically from data, derives inference rules from experience. It is shown through application that extracts new inference rules between hull dimensions and hull performance.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.57-73
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• 2006

This paper describes a methodology for automating the material handling equipment (MHE) evaluation and selection processes by combining knowledge-based rules and fuzzy multi-criteria decision making approach. The methodology is proposed to solve the MHE selection problems under fuzzy environment. At the primary stage, the most appropriate MHE type among the alternatives for each material flow link is searched. Knowledge-based rules are employed to retrieve the alternatives for each material flow link. To consider and compare the alternatives, multiple design factors are considered. These factors include both quantitative and qualitative measures. The qualitative measures are converted to numerical measures using fuzzy logic. The concept of fuzzy logic is applied to evaluation matrices used for the selection of the most suitable MHE through a fuzzy linguistic approach. Thus, this paper demonstrates the potential applicability of fuzzy theory in the MHE applications and provides a systemic guidance in the decision-making process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.329-332
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• 2007

• IE interfaces
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• v.3 no.1
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• pp.31-38
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• 1990

This paper deals with two topics which are vital in fuzzy expert systems; one is how to build fuzzy knowledge base by fuzzy expertise modeling for representing knowledge with imprecise characteristic and the other is how to draw an inference from fuzzy knowledge base using translating rules. The result of this study provides the basic principle for constructing the fuzzy knowledge base and the fuzzy inference system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.710-717
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• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.