• 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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• 2003.11c
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• pp.429-432
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• 2003

In this paper, a novel neural network approach is proposed for cleaning robot to complete coverage path planning with obstacle avoidance in stationary and dynamic environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location without any prior knowledge of the dynamic environment.

• Proceedings of the KAIS Fall Conference
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• 2008.11a
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.414-421
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• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.423-433
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• 1996

This study was conducted to predict the drafts of various tillage tools with a model tool and a neural network. Drafts of tillage tools were measured and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one step ahead prediction . The results showed the model tool draft had linear relations with high coefficient of determinations to the drafts of the tillage tools. Also, the drafts of tillage tools were successfully predicted by the developed neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.309-314
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• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.245-248
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• 2000

In this paper we improve the performance of autonomous mobile robot by induction of reinforcement learning concept. Generally, the system used in this paper is divided into two part. Namely, one is neural-fuzzy and the other is dynamic recurrent neural networks. Neural-fuzzy determines the next action of robot. Also, the neural-fuzzy is determined to optimal action internal reinforcement from dynamic recurrent neural network. Dynamic recurrent neural network evaluated to determine action of neural-fuzzy by external reinforcement signal from environment, Besides, dynamic recurrent neural network weight determined to internal reinforcement signal value is evolved by genetic algorithms. The architecture of propose system is applied to the computer simulations on controlling autonomous mobile robot.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of Magnetics
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• v.19 no.4
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• pp.315-322
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• 2014

In this work, Artificial Neural Network (ANN) was used to model the dynamic behavior of ferromagnetic hysteresis derived from performing the mean-field analysis on the Ising model. The effect of field parameters and system structure (via coordination number) on dynamic critical points was elucidated. The Ising magnetization equation was drawn from mean-field picture where the steady hysteresis loops were extracted, and series of the dynamic critical points for constructing dynamic phase-diagram were depicted. From the dynamic critical points, the field parameters and the coordination number were treated as inputs whereas the dynamic critical temperature was considered as the output of the ANN. The input-output datasets were divided into training, validating and testing datasets. The number of neurons in hidden layer was varied in structuring ANN network with highest accuracy. The network was then used to predict dynamic critical points of the untrained input. The predicted and the targeted outputs were found to match well over an extensive range even for systems with different structures and field parameters. This therefore confirms the ANN capabilities and indicates the ANN ability in modeling the ferromagnetic dynamic hysteresis behavior for establishing the dynamic-phase-diagram.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.421-424
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• 2003

This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.