• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.400-410
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• 1992

This paper deals with a design of neural network control architectures for robot motion. Three types of control architectures are designed as follows : 1) a neural network control architecture which has the same characteristics as computed torque method 2) a neural network control architecture for compensating the control error on computed torque method with fixed feedback gain 3) neural network adaptive control architecture. Computer simulation of PUMA manipulator with 6 links is conducted for robot motion in order to examine the proposed neural network control architectures.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• Journal of the Korean Ceramic Society
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• v.41 no.5
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• pp.353-358
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• 2004

Spray drying is a unique drying process for powder production. Spray dried product must be free-flowing in order to fill the pressing dies rapidly, especially in the ceramic production. The important powder characteristics are; the particle size distribu-tion and moisture content of the finished product that can be estimated and adjusted by the spray dryer operation, within limits, through regulation of atomizer and drying conditions. In order to estimate the moisture content of the resultant dried product, we modeled the control system of the drying process using two different Artificial Neural Network (ANN) approaches, namely the Back-Propagation Multiplayer Perceptron (BPMLP) algorithm and the Radial Basis Function (RBF) network. It was found out that the performance of both of the artificial neural network models were quite significant and the total testing error for the 100 data was 0.8 and 0.7 for the BPMLP algorithm and the RBF network respectively.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.114-116
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• 1993

A novel Neural networks controller for Buck type DC-DC converter is presented and compared with the operation of sliding node coupled several control strategies for the converter. The connection weights of neural networks are trained by error back propagation algorithm. The behavior of the control system that arises fred the use of those methods is analyzed from the viewpoint of dynamic and steady state errors and simulation results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.162.5-162
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• 2001

This paper presents a indirect vector control system for induction motors using an adaptive fuzzy logic(AFL) speed estimator. The proposed speed estimator is based on the MRAS(Mode Referece Adaptive System) scheme. In general, the MRAS speed estimation approaches are more simple than any other strategies. However, there are some difficulties in the scheme, which are strong sensitivity to the motor parameters variations and necessity to detune the estimator gains caused by different speed area. In this paper, the AFL speed estimator is proposed to solve the problems. The structure of the proposed AFL is very simple. The input of the AFL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed. Moreover, the back propagation algorithm is combined to adjust the parameters of the fuzzy logic to the most appropriate values during the operating the system. Finally, the validity of the ...

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.866-871
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• 1991

The multi-layer neural network possesses the desirable characteristics of parallel distributed processing and learning capacity, by which the uncertain variation of the parameters in the dynamically complex system can be handled adoptively. However the error back propagation algorithm that has been utilized popularly in the learning procedure of the mulfi-Jayer neural network has the significant limitations in the real application because of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1022-1027
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• 1991

A new method to control a robot manipulator by neural networks is proposed. The controller is composed of both a PD controller and a neural network-based feedforward controller. MLP(multi-layer perceptron) neural network is used for the feedforward controller and trained by BP(back-propagation) learning rule. Error terms for BP learning rule are composed of the outputs of a PD controller and the acceleration errors of manipulator joints. We compare the proposed method with existing ones and contrast performances of them by simulation. Also, We discuss the real application of the proposed method in consideration of the learning time of the neural network and the time required for sensing the joint acceleration.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.219-226
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• 2003

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory fir the adaptive control of linear systems, there exists relatively little general theory fir the adaptive control of nonlinear systems. Adaptive control technique is essential fir providing a stable and robust performance fir application of robot control. The proposed neuro control algorithm is one of teaming a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique f3r real-time control of robot system using DSPs.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.166-176
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• 1994

본 논문에서는 'back propagation' 신경망 알고리즘을 이용하여 자동 신용 평가 시스템을 개발하고 실제 데이타를 이용하여 이 시스템을 평가하여 보았다. 평가 과정중 신경망의 학습 수렴속도와 그의 여부는 학습에 이용된 데이타의 수에 따라 민감하게 변화한다는 것과 학습후 학습에 이용되지 않은 새로운 데이타들에 대한 신용 평가의 판별력과 학습에 이용된 데이타들에 대한 신용 평가의 판별력 사이에는 유의한 차이가 있음도 관찰되었다. 그리고, 학습에 이용된 데이타들의 갯수가 임의의 한 경계점을 넘어서면, 기존의 다른 많은 연구들이 주장했던 것과는 달리 학습 수렴 여부와 판별력이 급격히 떨어진다는 것도 관찰되었다. 또한 본 논문에서는 이상에서와 같이 관찰된 시스템 평가 결과를 신경망 이론의 학습 방법과 error space상에서 hyperplanes이 작용하는 역할의 관점에서 해석하였다.