• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1054-1057
• /
• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.359-362
• /
• 2003

In PDP(Plasma Display Panel), sub-field method is used to implement gray scale. Each sub-field has different periods. And Every gray level has information of which sub-field has to be displayed. This is called sub-field mapping. There are several sub-field mapping values in some gray levels. So, it is possible to select best choice in this paper, we propose new sub field mapping method using a fuzzy inference system to select best sub-field mapping values in accordance with input image and environment temperature. In order to implement fuzzy system, we used MATLAB fuzzy inference editor.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.436-439
• /
• 1995

In this paper we present a method to construct fuzzy model with multi-dimension input membership function, which can construct fuzzy inference system on one node of the network directly. This method comes from a common framework called Universal Learning Network (ULN). The fuzzy model under the framework of ULN is called Universal Learning Network-based Fuzzy Inference System (ULNFIS), which possesses certain advantages over other networks such as neural network. We also introduce how to imitate a real system with ULN and a control scheme using ULNFIS.

• Journal of Advanced Marine Engineering and Technology
• /
• v.23 no.1
• /
• pp.40-46
• /
• 1999

A self-tuning FPID(Fuzzy Proportional Intergral Derivative) controller fo load frequency control of 2-area power systemis proposed in this paper. The paramters of the proposed self-tuning FPID controller are self-tuned by the proposed fuzzy inference technique. Therefore in this paper the fuzzy inference technique of PID gains using PSGM(Product Sum Gravity Method) is presented and is applied to the load frequency control of 2-area power system. The computer simulation results show that the proposed controller give better more control characteristics than convention-al PID, FLC under load changes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.532-538
• /
• 1998

In this paper, a new adaptive fuzzy inference method using neural network based fuzzy reasoning is proposed to make a fuzzy logic control system more adaptive and more effective. In most cases, the design of a fuzzy inference system rely on the method in which an expert or a skilled human operator would operate in that special domain. However, if he has not expert knowledge for any nonlinear environment, it is difficult to control in order to optimize. Thus, using the proposed adaptive structure for the fuzzy reasoning system can controled more adaptive and more effective in nonlinear environment for changing input membership functions and output membership functions. The proposed fuzzy inference algorithm is called adaptive neuro-fuzzy control(ANFC). ANFC can adapt a proper membership function for nonlinear plant, based upon a minimum number of rules and an initial approximate membership function. Nonlinear function approximation and rotary inverted pendulum control system ar employed to demonstrate the viability of the proposed ANFC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.579-582
• /
• 2003

In this paper, we propose a 4-step inference method needed for constructing a natural language communication system. The method is used to obtain fuzzy quantifier Q′when QA is Fisr τ⇔ Q′(m′A) is mF is m"is τ is inferred (Q, Q′: quantifiers, A: fuzzy subject, m′, m": modifiers, y: fuzzy predicate, τ: truth qualifier). We show that Q′is resolved step by step for two types of Q, including a non-increasing type (few,...) and a non-decreasing type(most,...).

• Journal of the Korean Society of Industry Convergence
• /
• v.9 no.3
• /
• pp.173-178
• /
• 2006

For the design of multivariable fuzzy control systems decomposition of control rules is a efficent inference method since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper we define indices of applicability which decides whether the decomposition method can be applied to a multivariable fuzzy system or not.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.9
• /
• pp.430-438
• /
• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.2
• /
• pp.87-93
• /
• 2008

In this paper, we propose a hierarchical fuzzy based nutrition evaluation system that can analyze the individuals' nutrition status through the inference results generated by each layer. Moreover, a method to minimize the uncertainty of inference in the evaluated nutrition status is discussed. To show the effect of the uncertainty in fuzzy inference, we compared the results of nutrition evaluation with/without the certainty factor of rules on 132 people over the age of 65. From the experimental results, we can see that the evaluation method with the modified certainty factor provides better reliability than that of the general evaluation method without the certainty factor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.117-120
• /
• 2001

In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and then choose a number of terms called initial representative keywords (IRKS) from them through fuzzy inference. Then, by expanding and reweighting IRKS using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKS so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The results show that our approach outperforms the other approaches.