• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1626-1629
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• 2002

This paper describes novel methods to construct fuzzy inference rules with gradient descent. The present methods have a constructive mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create the rule unit at the nearest position from the input space, for the central value of the membership function in the antecedent part. The second is to create the rule unit which has the minimum width, for the width of the membership function in the antecedent part. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.

• The Transactions of the Korea Information Processing Society
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• v.2 no.5
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• pp.626-633
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• 1995

It is vary hard to identify the fuzzy rules and tune the membership functions of the fuzzy inference in fuzzy systems modeling, We propose a fuzzy neural network model which can automatically identify the fuzzy rules and tune the membership functions of fuzzy inference simultaneously using artificial neural networks, and modify backpropagation algorithm for improving the convergence. The proposed method is verified by the simulation for a robot manipulator.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2750-2752
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• 2000

In this paper, Intelligent PLC for automatic warehousing system is presented. Intelligent PLC is that general PLC is incorporate the knowledge and experience of Expert and control method. This system is consist of sensor part, inference part and automatic warehousing robot part. this system recognize the Work by itself and move it to the appointed place according to the inference rule. The PLC program is constructed by LD(ladder diagram). Real test system was constructed and was driven to apply this program.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.505-507
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• 1998

In this paper, a simple hierarchical fuzzy inference system using structured Takagi-Sugeno type fuzzy inference units(SFIUs) is proposed. The number of fuzzy rules of the proposed HFIS is minimum in the sense of that only the number of partitions of each system variables, not of intermediate outputs of layered fuzzy controllers, are concerned. And resulted number of fuzzy rules is a summation of partition in each system variables. Gradient descent algorithm is used for adaptation of fuzzy rules. The ball and beam control is performed in computer simulation to illustrate the performance of the proposed controller.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.31-41
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• 1993

In this paper, a fuzzy adaptive controller is proposed for the process with large delay time and unmodelled dynamics. The fuzzy adaptive controller consists of self tuning controller and fuzzy tuning part. The self tuning controller is designed with the continuous time GMV (generalized minimum variance) using emulator and weighted least square method. It is realized by the hybrid method. The controller has robust characteristics by adapting the inference rule in design parameters. The inference processing is tuned according to the operating point of the process having the nonlinear characteristics considering the practical application. We review the characteristics of the fuzzy adaptive controller through the simulation. The controller is applied to practical electric furnace. As a result, the fuzzy adaptive controller shows the better characteristics than the simple numeric self tuning controller and the PI controller.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.243-251
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• 2014

In inference fit assembly process of the industrial robot, it basically needs the force data. One of the typical methods to get the force data is attaching torque sensors on the robot arm joint or end effector. This is effective way to reduce time delay and to improve preciseness of force control, but this method has several problems. To solve that problem, this paper suggests method which measures assembly force without torque sensor by using the sliding perturbation observer(SPO) and assembly process based on SPO to assemble successfully in inference assembly

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1414-1417
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• 1993

Wang and Medel proved (1991) that fuzzy systems with product inference, centroid defuzzification, and everywhere positive membership functions (in particular, Gaussians, Wang, 1992) are capable of approximating any real continuous control function on a compact set to arbitrary accuracy. Kosko (1992) proved that fuzzy systems, in which membership functions have compact support, and combination operation (V-operation) for rules is the sum, are also universal approximators. In this paper, we generalize this result of Kosko and prove that for any &- and V-operations, any defuzzification procedure, and any basic membership function with a compact support, the resulting fuzzy controls are universal approximators. Also, Wang's result is transfered to min-inference.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.67-76
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• 1995

In recent research on the fuzzy controller, the crisp type fuzzy controller model, in which the consequent part of the fuzzy control rules are crisp real numbers instead of fuzzy sets, due to its simplicity in calculation, has been widely used in various applications. In this paper we try to analyze the dynamical behavior of the crisp type fuzzy controller with both inference methods of min-max compositional rule and product-sum inference. The analysis reveals that a crisp type fuzzy controller behaves approximately like a PD controller.

• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.55-62
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• 2014

A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM), where the behavior of software failure is assumed to follow a non-homogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.105.3-105
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• 2001

In this paper, a current mode two-input maximum (Max) and minimum (Min) operations scheme, which is a useful building block for analog fuzzy inference systems, is presented. The Max and Min operations are incorporated in the same scheme with parallel processing. The proposed scheme comprises a MOS class AB/B configuration and current mirrors. Its simple structure can provide a high efficiency. The performance of the scheme exhibits a very sharp transfer characteristic and high accuracy. The proposed scheme achieves a high-speed operation and is suitable for real-time systems. The simulation results verifying the performances of the scheme are agreed with the expected values.