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이상부 제주한라대학 2002 論文集 Vol.26 No.-
At the on-line control method, fuzzy controller is stronger to the disturbance than the classical controller and its overshoot of the initialized value is excellent. The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. The fuzzy controller has good performance than the classical controller. Since the once determined control rule is fixed, it can't adjust to the environmentd changes of the control system, the controller output value has a minute error and it can't converge correctly to the desired value. This paper realizes the fuzzy controller by fuzzy implicatilon and inference. This paper analyzes the overshoot, convergence speed, rising time, error of steady state by gain influence.
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비례와 퍼지 제어기를 결합한 Hybrid제어기의 특성에 관한 연구
이상부 제주한라대학 2000 論文集 Vol.24 No.-
This paper investigates the characteristics of the Fuzzy controller and the proportional controller, and also examines and presents the complemently points of each controller. The proportional controller's desirable control is more precise than the Fuzzy controller's, and its response speed is also faster, but its initial value overshoot response is extremely unstable compared to the proportional, the Fuzzy controller's response speed is slow, its command's precise convergent is also not that good but its initial value response characteristic is superior. This paper investigates the characteristic of these controllers through simulation and presents each controller's complemently points.
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이상부 제주한라대학 1996 論文集 Vol.20 No.-
A Fuzzy Logic Controller is strong at disturbance. It can get a proper control quantity through the Fuzzy inference in case a mathmatical modeling is impossible on accaint of the complication of a control object, and easily come to the desired value withiout a big overshoot at the initialized value. But the fuzzy controller has a difficulty in qetting a precise controlling because it always has the error of a normal state at the vicinity of the desired position. This paper will show the fuzzy neural network controller which can get a precise controlting after the neural network with the ability of a learning is imported to the fuzzy controller and the error of a normal state, a fault of the fuzzy controller, can be eliminated, and will compare this controller with the fuzzy controller.
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이상부 제주한라대학 1999 論文集 Vol.23 No.-
The disturbance of the FLC(Fuzzy Logic Controller) is superior to a classical controller and its overshoot of the initialized value is excellent. The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. But it has a limit to make the control rule of the fuzzy controller through an expert's experience. Since the once determined control rule is fixed, it can't adjust to the environment changes of the control system, the controller output value has a minute error and it can't converge correctly to the desired value. There are many methods to eliminate the minute error, but this paper suggests the EP-FNNIC(Fuzzy Neural Network Intelligence Controller) which combines FLC with NN(Neural Network) and EP(Evolution Programming). The output charactereistics of EP-FNNIC will be compared and analyzed with FLC. It will be showed that this EP-FNNIC converge correctly to the desirable value without any error. The properties of these two kinds of controllers such as the convergence speed, overshoot, rising time and error of steady state also will be compared.
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이상부 제주한라대학 1994 論文集 Vol.18 No.-
The purpose of the study is to analyze the attributes of both the supervisor learning method and unsupervisor learning method from the comparative a characteristic. The learning error, active value of neuron, and output value of output neuron resulted from each method are analyzed. At the same time, the critical information created from the learning process are shown up on the monitor of the PC. The study makes it easier to compare 3 variables-learning error, active value of neuron and output value of output neuron-by representing them graphically.
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이상부 제주한라대학 1998 論文集 Vol.22 No.-
The FLC (fuzzy logic controller) is a simple control method introduced to solve problems of the old PID controller. FLC is more resistant to disturbance than a classical controller and its overshoot of the initialized value is excellent. The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. But fuzzy controller's operation under expert has a limit. Because the once determined control rule is fixed, it can't adjust to the environmental changes of the control system. The controller output value has a minute possibility of error and sometimes can't converge correctly to the desired value. There are many ways to eliminate the minute error^(1)2)3)4)), but this paper suggests the EP - FNNIC (evolution programming - fuzzy neural network intelligent controller) intelligence controller which combines FLC with NN (neural network) and EP. The output characteristics of the EP - FNNIC controller will be compared and analyzed with EP - fuzzy and FLC. It will be shown that this EP-FNNIC controller converges correctly to the desirable value without any error.
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이상부 제주한라대학 1997 論文集 Vol.21 No.-
The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. But to make the control rule of the fuzzy controller through an expert's experiance has a limit. Because the once determined control rule is fixed, it can't adjust to the environment changes of the control system, the controller output value has a minute error and it can't convergence correctly to the desired valu^(1)2)). There are many ways to eliminate the minute error^(3)4)5)), but in this paper suggests EP - FNNIC (Fuzzy Neural Network Intelligence Controller) intelligence controller which combines FLC with NN (Neural Network) and EP (Evolution Programming). The output characteristics of EP - FNNIC controller will be compared and analyzed with FLC. It will be showed that this EP - FNNIC controller converge correctly to the desirable value without any error. The convergence speed, overshoot, rising time, error of steady state of controller of these two kinds also will be compared.
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