• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1149-1152
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• 1999

In this paper we analyzed each region of specific points and ｅ-Δｅphase plane in order to make fuzzy rule base. After we composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. The composed method are converged more rapidly than single rule base in convergence region. Proposed method is alternately use at specific points of ｅ-Δｅphase plane with two fuzzy control rules, that is one control rule occruing the steady state error used in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Two fuzzy control rules in the ｅ-Δｅ phase plane decide the change time according to response characteristics of plants. As the results of simulation through the second order plant and the delay time plan, Proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.333-336
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• 2002

In many cases, fuzzy modeling has a defect that the design procedure cannot be theoretically justified. To overcome this difficulty, we suggest a new design method for fuzzy model by combining genetic algorithm(GA) and mush set theory. GA, which has the advantages is optimization, and rule base. However, it is some what time consuming, so are introduce rough set theory to the rule reduction procedure. As a result, the decrease of learning time and the considerable rate of rule reduction is achieved without loss of useful information. The preposed algorithm is composed of three stages; First stage is quasi-optimization of fuzzy model using GA(coarse tuning). Next the obtained rule base is reduced by rough set concept(rule reduction). Finally we perform re-optimization of the membership functions by GA(fine tuning). To check the effectiveness of the suggested algorithm, examples for time series prediction are examined.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.63-73
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• 2003

Insurance system has a lot of decision factors which are affected by the kinds of insurance products. and has the features of many exceptions. Since in applying the product attributes to the current system the value definition through tables and the exception treatment logic (if then else) are used in parallel, the cost of a product change and a new product development becomes increased and the prompt market reaction is difficult. In this paper, we propose the well formed rule base system which makes data for the business logic of insurance attributes and discuss the benefit of application of this system to the real project.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.179-190
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• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.410-412
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• 1998

Subsymbolic Knowledge processing is said to be changed states of networks constructed from small elements. subsymbolic systems also make it possible to use connectionist models for knowledge processing. Connectionist realization such modulus are modulus linked together for solving a given problem. We study using neural networks as distinct actions. The output vectors produced by the neural networks are consider as a new facts. These new facts are then processed to activate another networks or used in the current production rule, The production rule is applying knowledge stored in the knowledge base to make inference. After neural networks knowledge base is constructed and trained. We present a running sample of incorporating neural network knowledge base. We implement using rochester connectionist simulator. We suggest that incorporating neural network knowledge base. Therefore incorporated neural network knowledge base ensures a cleaner solution which results in better perfor s.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.970-974
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• 1993

This paper is concerned with an automatic generation of BOM (Bill Of Material) for a bicycle frame set using a knowledge based system. The major components module system includes : (1) Part information retrieval in CAD drawing, (2) BOM code generation rule, and (3) Database interface. The knowledge based system includes a rule base and a fact base. The fact base consists of basic, variant, and optional components of the standard BOMs of frame sets. The rule contains rules for generating new BOM code in case that the specified is not in the database. The system was implemented on a SUN workstation under Open Windows environments. AutoCAD for CAD drawing was also used.

• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.52-59
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• 1999

The CAD/CAM System for deep drawing transfer die tin mechanical press process has been developed. The developed CAD system can generate the drawing of transfer die in mechanical press. Using thee results from CAD system, it can generate the NC data to machine die's elements on the CAD system. This system can reduce design man-hour an human errors. In order to construct the system, it is used to automated the design process and generate the NC data using concepts of the designing rule and the machining rule. The developed system is based on the knowledge base system which is involved a lot of expert's technology in the practice field. Using AutoLISP language under the AutoCAD system, CTK customer language of SmartCAM is used as the overall CAD/CAM environment. Results of this system will be provide effective aids to the designer and manufacturer in this field.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.441-446
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• 1992

We propose a control architecture for the fuzzy logic control of robot manipulators and a rule base derivation method for a fuzzy logic controller(FLC) using a neural network. The control architecture is composed of FLC and PD(positional Derivative) controller. And a neural network is designed in consideration of the FLC's structure. After the training is finished by BP(Back Propagation) and FEL(Feedback Error Learning) method, the rule base is derived from the neural network and is reduced through two stages - smoothing, logical reduction. Also, we show the performance of the control architecture through the simulation to verify the effectiveness of our proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.153-156
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• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.