• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.186-190
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• 2006

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.413-416
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• 1997

Recently, dynamic recurrent neural networks(DRNN) for identification of nonlinear dynamic systems have been researched extensively. In general, dynamic backpropagation was used to adjust the weights of neural networks. But, this method requires many complex calculations and has the possibility of falling into a local minimum. So, we propose a new approach to identify nonlinear dynamic systems using DRNN. In order to adjust the weights of neurons, we use evolution strategies, which is a method used to solve an optimal problem having many local minimums. DRNN trained by evolution strategies with mutation as the main operator can act as a plant emulator. And the fitness function of evolution strategies is based on the difference of the plant's outputs and DRNN's outputs. Thus, this new approach at identifying nonlinear dynamic system, when applied to the simulation of a two-link robot manipulator, demonstrates the performance and efficiency of this proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.60-64
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• 1995

The conventional neural network models are a parody of biological neural structures, and have very slow learning. In order to emulate some dynamic functions, such as learning and adaption, and to better reflect the dynamics of biological neurons, M.M. Gupta and D.H. Rao have developed a 'dynamic neural model'(DNU). Proposed neural unit model is to introduce some dynamics to the neuron transfer function, such that the neuron activity depends on internal states. Integrating an dynamic elementry processor within the neuron allows the neuron to act dynamic response Numerical examples are presented for a model system. Those case studies showed that the proposed DNU is so useful in practical sense.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1281-1284
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• 1998

This paper proposes an efficient time series prediction of the nonlinear dynamical discrete-time systems using multilayer neural networks of a hybrid learning algorithm. The proposed learning algorithm is a hybrid backpropagation algorithm based on the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The proposed algorithm has been applied to the y00 samples of 700 sequences to predict the next 100 samples. The simulation results shows that the proposed algorithm has better performances of the convergence and the prediction, in comparision with that using backpropagation algorithm based on the gradient descent for multilayer neural network.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.238-242
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• 2013

We consider the problem of constructing observers for nonlinear systems with unknown inputs. Connectionist networks, also called neural networks, have been broadly applied to solve many different problems since McCulloch and Pitts had shown mathematically their information processing ability in 1943. In this thesis, we present a genetic neuro-control scheme for nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.384-388
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• 1996

The closed-loop system modeling of an Active/semiactive suspension system has been accomplished through an artificial neural Networks. The 7DOF full model as the system equation of motion has been derived and the output feedback linear quadratic regulator has been designed for the control purpose. For the neural networks training set of a sample data has been obtained through the computer simulation. A 7DOF full model with LQR controller simulated under the several road conditions such as sinusoidal bumps and the rectangular bumps. A general multilayer perceptron neural network is used for the dynamic modeling and the target outputs are feedback to the input layer. The Backpropagation method is used as the training algorithm. The modeling of system and the model validation have been shown through computer simulations.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.769-775
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• 2011

Custom power devices such as dynamic voltage restorer (DVR) and DSTATCOM are used to improve the power quality in distribution systems. These devices require real power to compensate the deep voltage sag during sufficient time. An interline DVR (IDVR) consists of several DVRs in different feeders. In this paper, a neural network is proposed to control the IDVR performance to achieve optimal mitigation of voltage sags, swell, and unbalance, as well as improvement of dynamic performance. Three multilayer perceptron neural networks are used to identify and regulate the dynamics of the voltage on sensitive load. A backpropagation algorithm trains this type of network. The proposed controller provides optimal mitigation of voltage dynamic. Simulation is carried out by MATLAB/Simulink, demonstrating that the proposed controller has fast response with lower total harmonic distortion.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.500-503
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• 1990

Neural network is a computational model of the biological nervous system developed to exploit its intelligence and parallelism. Applying neural networks to robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 arm shows that it can move at high speed as well as adapt to unforseen load changes. The results are compared with the conventional PD control scheme.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.311-315
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• 2006

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin