• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.13-18
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• 1996

A real-time identifier for the nonstationary time-varying signals and systems was implemented using a low cost DSP (digital signal processing) chip. The identifier is comprised of I/O units, a central processing unit, a control unit and its supporting software. In order t estimate the system accurately and to reduce quantization error during arithmetic operation, the firmware was programmed with 64-bit extended precision arithmetic. The performance of the identifier was verified by comparing with the simulation results. The implemented real-time identifier has negligible quantization errors and its real-time processing capability crresponds to 0.6kHz for the nonstationary AR (autoregressive) model with n=4 and m=1.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1670-1679
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• 1995

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.293-295
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• 1993

In this paper, Neural-Network Identifier that has time-delay element, error limit and small weighting factor is proposed. A proposed identifier has good performance to identify non-linear system with noise. To test the effectiveness of the algorithm presented above, the simulation for output tracking of non-linear system is implemented.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.243-247
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• 1992

This paper describes a neural network based control scheme with MRAC. The system consists of two neural network; one is for identifier and the other is for controller. Identification is firstly performed to learn the behavior of the nonlinear plant. Neural net controller is next trained by backpropagating the error at the output of plant through the identifier. Also the training method used in this paper repeatedly updates weights of neural network to track the reference model.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.809-812
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• 1999

The pendulum is a SIMO(Single-input multi-output) system that both angle of pendulum and position of cart controlled simultaneously by one actuator. In this paper, propose a hybrid neuro-controller to apply to pendulum system. We design the conventional optimal controller and the neural network as a identifier, which can identify the uncertainty of plant not modeled, respectively. Then we combine them into a novel controller, with a structure that the error between plant and identifier is added in conventional optimal control input Finally, the paper shows the validity of the proposed controller through computer simulations and experiments.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.519-526
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• 1990

In this paper we propose the resolved motion controller using a neural network for a robot manipulator. Neural identifier designed by a neural network is trained by using a feedback force as an error signal. The identifier approximates the output of a unknown nonlinear system by monitoring both the input and the output of this system. If the neural network is sufficiently trained well, it does not require either strict modelling of the manipulator or precise parameter estimation. The effectiveness of the proposed controller is demonstrated by computer simulation using a two-link planar robot.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.740-747
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• 1988

This paper describes the parameter convergence property for an adaptive identifier and deals with the stability of the adaptive system in terms of the general error model. The Persistent Excitation (PE) condition to guarantee parameter convergence is derived using the Power Spectrum Analysis. In the adaptive identifier designed under the assumptions that the plant has not unmodelled dynamics, it can be shown that the equilibrium points of adjustable parameters are independent on the position or the number of input spectrums, if the adaptive signal is PE. When the plant contains unmodelled dynamics and the same controller is used, the PE condition can still hold but the parameter tuned values are changed with the spectrum.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.4
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• pp.1-9
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• 1983

In designing the adaptive recursive filter using the MRAS hyperstable output-error identifier, the most crucial point is the design of the error-smoothing filter In this paper, it is shown that by modifyins the HARF algorithm properly, we can obtain an algorithm which requires no error-smoothing filter and has relatively high convergence rate.