• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.175-178
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• 2008

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. But the most of fuzzy systems are difficult to make fuzzy inference rules in the case of MIMO system. The past days, We had proposed the MIMO fuzzy inference which had extended a Z. Cao's fuzzy inference to handle MIMO system. But many times and effort needed to determine the relation matrix elements of MIMO fuzzy inference by heuristic and trial and error method in order to improve inference performances. In this paper, we propose a MIMO fuzzy inference method with the learning ability witch is used a gradient descent method in order to improve the performances. Through the computer simulation studies for the inverse kinematics problem of 2-axis robot, we show that proposed inference method using a gradient descent method has good performances.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.7-13
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• 2002

Almost applications using fuzzy theory are based on the fuzzy inference. However fuzzy inference needs much time in calculation process for the fuzzy system with many input variables or many fuzzy labels defined on each variable. Inference time is dependent on the number of arithmetic Product in computation Process. Especially, the inference time is a primary constraint to fuzzy control applications using microprocessor or PC-based controller. In this paper, a simple fast fuzzy inference algorithm(FFIA), without loss of information, was proposed to reduce the inference time based on the fuzzy system with triangular membership functions in antecedent part of fuzzy rule. The proposed algorithm was induced by using partition of input state space and simple geometrical analysis. By using this scheme, we can take the same effect of the fuzzy rule reduction.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.4
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• pp.33-43
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• 2023

In this study, it was developed and verified an application method of fuzzy-logic theory to the rating process of agricultural fill dam safety. A fuzzy-logic is very famous logical system when some decision making is made on the status of a lack of information. Three proxies were selected and configured membership functions (MFs) and these MFs were activated in the process of fuzzification procedures. Fuzzified vlaues were passed through the rule-based inference system, then fire strength could classified among cases of the rule-based inference system. To obtain final results, Mandani-type was adapted in the defuzzification process. As the results, it was shown the developed system can give a correct results that was compared with Matlab - fuzzy inference function. More ever it could perform the detailed analysis and improvement on the infrastructure safety rating process using classical diagnosis method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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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.

• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.75-81
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• 2007

Previous queue inference has been studied with some limits. Larson's inference engine, which is the basis for this paper, also processed with basic assumption that arrival process is poisson process. Our inference method, which relaxes the poisson process assumption, must be a useful tool for looking into unobservable inside of queueing systems, as well as calculating accurate system performance. This paper employs these inference methods and proves the validity. Then we apply this method to system analysis for more complicated models. At first, we suggest methods to system with known number of servers, then expand to unknown number of servers. For validating our inference approach, we run some simulation models and compare true values with our results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.2
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• pp.198-206
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• 2013

This paper presents the improvement of tracking performance using fuzzy inference system based task prioritizations for multi-function radars. The presented technique calculates elemental priorities using track information of a target and obtain the total priority from fuzzy inference system of each fuzzy set's membership function. In this paper, we proposed the task prioritization algorithms based on fuzzy inference system, and evaluated the tracking performance on multi-function radar scenario using it. As a result, we confirmed that excellent performance could be achieved when using the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.6
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• pp.613-620
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• 1992

Recently, there are lots of research work on fuzzy Information theory for many practlcal applications. As the fuzzy control systems become to be sophisticated, they demand more fuzzy parameters, membership functions and fuzzy Inference rules. Eventually, they need effective parallel computing architectures to implement those complex fuzzy inference rules. In this paper, a optical fuzzy Inference system based on 2-D spatial light modulator and digital image board Is Implemented as a new approach for real-time parallel fuzzy computing system. From its good experimental results on the practical fuzzy airconditioner system, a new real-time Opto Fuzzy Inference system Is suggested.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.764-770
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• 2001

In this paper, we propose a novel PCI bus based parallel fuzzy inference system for transferring and inferencing the large volumes of fuzzy data in high speed. For this, the PCI 9050 interface chip is used to connect a local bus design as a PCI target core using FPGA to the PCI bus. We design and implement the PCI target core by using VHDL to be processed in parallel by considering the points of parallelyzing each element of the membership functions and each block of the condition and/or consequent parts. The proposed system can be used in a system requiring a rapid inference time in a real-time system or pattern recognition on the large volume of satellite images that have many inference variables in the condition and consequent parts.