• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.184-191
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• 2001

This paper proposes a prefilter type velocity compensating control system using modified chaotic neural networks for the trajectory control of robotic manipulator. Since the structure of modified chaotic neural networks(MCNN) and neurons have highly nonlinear dynamic characteristics, MCNN can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis robot manipulator is designed by MCNN. The MCNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on-line learning and the performance is excellent. The MCNN controller showed much better control performance and shorter calculation time compared to the RNN controller, Another advantage of the proposed controller could by attached to conventional robot controller without hardware changes.

• The Korean Journal of Ecology
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• v.23 no.2
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• pp.89-100
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• 2000

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.21-25
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• 1996

In this paper we suggest a general purpose RNN training algorithm which is derived on the optimal control concepts and variational methods. First, learning is regared as an optimal control problem, then using the variational methods we obtain optimal weights which are given by a two-point boundary-value problem. Finally, the modified gradient descent algorithm is applied to RNN for on-line training. This algorithm is intended to be used on learning complex dynamic mappings between time varing I/O data. It is useful for nonlinear control, identification, and signal processing application of RNN because its storage requirement is not high and on-line learning is possible. Simulation results for a nonlinear plant identification are illustrated.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.3-10
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• 1995

This paper proposedan on-line learning controller which can be applied to nonlinear systems. The proposed on-line learning controller is based on the universal approximation by the local affine mapping-based neural networks. It has self-organizing and learning capability to adapt itself to the new environment arising from the variation of operating point of the nonlinear system. Since the learning controller retains the knowledge of trained dynamics, it can promptly adapt itself to situations similar to the previously experienced one. This prompt adaptability of the proposed control system is illustrated through simulations.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.530-532
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• 1998

In this paper, We Designed the torque compensatory controller for robot manipulator using modified chaotic neural networks with self feedback loop. The proposed torque compensatory controller compensate torque of the PD controller. In order to estimate the proposed controller, we implemented to the Cartesian space control of three-axis PUMA robot and compared the simulation results with recurrent neural networks(RNNs) controller. Simulation results show that the learning error drastically decrease at on-line learning. The proposed CNNs controller shows much better control performance and shorter processing time compared to the recurrent neural network controller in the robot trajectory control.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.92-99
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• 2004

In this paper, the decoupled neural network reference compensation technique (DRCT) is applied to the control of a two degrees-of-freedom inverted pendulum mounted on an x-y table. Neural networks are used as auxiliary controllers for both the x axis and y axis of the PD controlled inverted pendulum. The DRCT method known to compensate for uncertainties at the trajectory level is used to control both the angle of a pendulum and the position of a cart simultaneously. Implementation of an on-line neural network learning algorithm has been implemented on the DSP board of the dSpace DSP system. Experimental studies have shown successful balancing of a pendulum on an x-y plane and good position control under external disturbances as well.

• The Journal of Information Technology
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• v.9 no.1
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• pp.55-63
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• 2006

In this paper, a Hangul recognition system using of Kohonen Network in the phoneme separation and learning is proposed. A Hangul consists of phoneme that are consists of strokes. The phoneme recognition and separation are very important in the recognition of character. So, the phonemes which mismatching has been happened are correctly separated through the learning of neural networks. also, learning rate($\alpha$) adjusted according to error, in order to solved that its decreased the number of iteration and the problem of local minimum, adaptively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.147-153
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• 1995

In this paper, we propose the position- and size-independent handwritten on-line Korean character recognition system using multilayer neural networks which are trained with error back-propagation learning algorithm and the features of Hanguel consonants and vowels. Starting point, end point, and three vectors from starting point to end point of each stroke of characters inputted from mouse or tablet are applied as inputs of neural networks. If double consonants and vowels are separated by single consonants and vowels, all consonants and vowels have at most four strokes. Therefore, four neural networks learn the consonants and the vowels having each number of strokes. Also, we propose the algorithm of separating the consonants and vowels and constructing a character.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.492-494
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• 1993

CMAC (Cerebeller Model Articulation Controller) is kind of Neural Networks that imitate the human cerebellum. For storage and retrieval of learned data, the input of CMAC is used as a key to determine the memory location. he learned information is distributively stored in physical memory. The learning of CMAC is very fast and converged well, therefore, it effects the application of Pattern Recognition. Through the our experiment of Pattern Recognition, we will prove that CMAC is very suitable for On-line real time processing and incremental learning of Neural Networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.105-111
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• 1999

This Paper is about how to deside the best parameter of PID controller, using Genetic Algorithms and Neural Networks. Control by Genetic Algorithms, which is off-line pass, has weakness for disturbance. So we want to improve like followings by adding Neural Network to controller and putting it on line. First we find PID parameter by Genetic Algorithms in forward pass of Neural Network and set the best output condition according to the increasing number of generation. Second, we explain the adaptability for disturbance with simulation by correcting parameter by backpropagation learning rule by using the learning ability of Neural Network.