• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.129-134
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• 1992

Fuzzy theory has shown good control performance for non-linear system that is difficult to be controlled by the conventional controller. Backpropagation neural network can interpolate output without the priori knowledge of its dynamics. In this paper, we proposes a Fuzzy-Neural Controller. The Fuzzy Control by deterministic rule may not be sensitive for uncertain conditions and has a disadvantage of setting the rule by repeatedly experience. To solve such problems, we construct Self organizing Fuzzy-Neural Controller which can reorganize the fuzzy rule according to the state of system. Experimental results show that proposed Fuzzy-Neural Controller has better performance than conventional controller(PID) has especially rising time and overshoot characteristics.

• 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.

• Transactions of Materials Processing
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• v.17 no.3
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• pp.161-169
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• 2008

In the present work, the two-back stress model is proposed and continuum damage mechanics (CDM) is incorporated into the plastic constitutive relation in order to describe the plastic deformation localization and the damage evolution in a deforming continuum body. Coupling between damage mechanics and isothermal rate independent plasticity is performed using the kinematic hardening rule, which in turn is formulated by combining the nonlinear Armstrong-Frederick rule and the Phillips rule. The numerical analyses are carried out within h deformation theory. It is noted that the damage evolution within a work piece accelerates the plastic deformation localization such that the material with lower hardening exponent results in a rapid shear band formation. Moreover, the results from the numerical analysis reflected closely with the micro-structures around the fractured regime. The effects of the various hardening parameters on deformation localization are also investigated. As the nonlinear strain rate description in the back stress evolution becomes dominant, the strain localization becomes intensified as well as the damage evolution.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.374-376
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• 2001

In this paper, we developed the fault diagnostic expert system of silicone-immersed transformer using dissolved gas analysis. The knowledge base module consists of the knowledge using the rule: if Then . The inference engine uses the fuzzy rule for the management of uncertainty of the boundary and rule and derivate the Belief and Plausibility of the normality and fault using Dempster-Shafer theory. The expert system is connected to the database and it can manages the history of gas-data of the transformer.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• Journal of Welding and Joining
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• v.22 no.2
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• pp.28-32
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• 2004

Fatigue life at seam weldment of thick plate was estimated using the finite element and FEM-FAT(an exclusive fatigue solver). Finite element meshing at toe and root of weldment was based oil Radaj's theory. Also, the results of FE analysis were compared with experimental results in the point of Miner's Rule. The results of FE and FEM-FAT analysis were in accord with experimental results within 60% confidence. This result reveals that above techniques is useful in assessment of seam weldment and to be an alternative method instead of an object experiment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.49-52
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• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.1975-1983
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• 1989

Implementing the linguistic theories on computer, we resolve the problems for restrictions of computer and increase processing efficiency for systemization not for linguistic theory itself. Thus, we modify the grammatical theory to be applied to systems. This paper reports the various problems about constructing dictionaies, defining rules, and appling universal principles and metarules, which is caused to implement the systems based on GPSG. In semantic interpretations, logical expressions which correspond Montague grammar are acquired, and we make a rule connect with several logical expressions. And we show the efficiency of the this method through implementing parser.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.273-278
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• 1998

Belief network(or Bayesian network) based on Bayes' rule in probabilistic theory can be applied to the reasoning of diagnostic systems. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1149-1158
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• 2004

In this thesis, we created a Fuzzy rule in a Fuzzy logic that are Fuzzy logic which is composed of linguistic rules and Fuzzy inference engine for effective traffic control in ATM networks. The parameters of the Fuzzy rules are adapted to minimize the given performance index in both cases. In other words, the difuzzification value controls the threshold in the buffer to arrival ratio to traffic priority (low or high) using fuzzy set theory for traffic connected after reasoning. Also, show experiment result about rule by MATLAB6.5 and on-line bulid-up to verify validity of created Fuzzy rule. As a result, we can verify that threshold value in buffer is efficiently controlled by the traffic arrival ratio.