• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.139-152
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• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.61-64
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS32OC50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator. In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters are determined by the pole-placement method. Performance of self-tuning adaptive controller is illustrated by the simulation and experiment for a SCARA robot.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.7 no.1
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• pp.26-35
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• 1996

In this paper, characteristics of the electromagnetically coupled broadband microstrip antennas are analyzed by the Finite Difference Time Domain (FDTD) method, and antenna para- meters are optimized to get maximum bnadwidth. By using short radial tuning stub in microstrip feedline, electromagnetically coupled microstrip antenna shows broadband ($\simeq$13%) characteristics, and the characteristics are varied as a function of radius, radial angle, and position of the radial tuning stub. Operating frequency, return loss, VSWR and input impedance are calculated by Fourier transforming the time domain results. After optimization of the parameters, maximum bandwidth of the radial stub tuning microstrip antenna is about 15% and the ripple char- acteristic of the VSWR is better than the rectangular tuning stub microstrip antenna.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.3
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• pp.1-10
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• 2011

Proportional.Integral.Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, and Particle Swarm Optimization and Ant colony optimization. The proposed algorithm is used to tune the PID parameters and its performance has been compared with the conventional methods like Ziegler Nichols and Lambda method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on Machine Direction of basics weight control in pulp and paper industry. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller, in terms of tracking set point, is also compared and simulation results are shown.

• Journal of KSNVE
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• v.11 no.1
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1242-1244
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• 1992

This Paper deals with a Auto-Tuning Fuzzy PID Controller used in real time and its application for induction motor. The control strategy of the controller is able to develop and improve automatically. The new Auto-Tuning Fuzzy PID Control algorithm which modifies the fuzzy control decision table is presented in this paper. It can automatically refine an initial approximate set of fuzzy rules. The possibility of applying fuzzy algorithms in faster response, and more accurate was compared with other industrial processes, such as AC Motor driver. The performance of Proportional_Integral Derivative(PID) control and this fuzzy controllers is compared in terms of steady_state error, settling time, and response time. And then, Limitations of fuzzy control algorithms are also described.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.24-32
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• 2003

The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.948-955
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• 2008

The conventional PID controller has been widely employed in industry. However, the PID controller with one degree of freedom(DOF) can not optimize both set-point tracking response and disturbance rejection response at the same time. In order to solve this problem, a few types of 2DOF PID controllers have been suggested. In this paper, a tuning formula for a 2DOF PID controller is presented. The optimal parameter sets of the 2DOF PID controller are determined based on the first-order plus time delay process and a real-coded genetic algorithm(RCGA) such that the ITAE performance criterion is minimized. The tuning rule is then addressed using calculated parameter sets and another RCGA. A set of simulation works are carried out on three processes with time delay to verify the effectiveness of the proposed rule.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.885-890
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• 2004

Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature, the change of the dynamic characteristics in the reheater. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the Controller with disturbance rejection for thermal power plant using immune based multiobjective approach. An ITSE(Integral of time weighted squared error) is used to decide performance of tuning results.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.