• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• 한국CDE학회논문집
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• 제3권1호
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• pp.57-67
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• 1998

Genetic programming (GP) is an extension of a genetic algoriths paradigm, deals with tree structures representing computer programs as individuals. In recent, there have been many research activities on applications of GP to various engineering problems including system identification, data mining, function approximation, and so forth. However, standard GP suffers from the lack of the estimation techniques for numerical parameters of the GP tree that is an essential element in treating various engineering applications involving real-valued function approximations. Unlike the other research activities, where nonlinear optimization methods are employed, I adopt the use of a weighted linear associative memory for estimation of these parameters under GP algorithm. This approach can significantly reduce computational cost while the reasonable accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the tree with the desired accuracy. This motivates to devise a group of additive genetic programming trees (GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the teaming and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of this approach is verified by applying the GAGPT to the estimation of the principal dimensions of bulk cargo ships and engine torque of the passenger car.

• 전기학회논문지
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• 제58권6호
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• pp.1223-1229
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• 2009

The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.

• Kyungpook Mathematical Journal
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• 제64권1호
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• pp.95-111
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• 2024

The filled function method is useful in solving unconstrained global optimization problems. However, depending on the type of function, and parameters used, there are limitations that cause difficultiies in implemenations. Exponential and logarithmic functions lead to the overflow effect, requiring iterative adjustment of the parameters. This paper proposes a polynomial-filled function that has a general form, is non-exponential, nonlogarithmic, non-parameteric, and continuously differentiable. With this newly proposed filled function, the aforementioned shortcomings of the filled function method can be overcome. To confirm the superiority of the proposed filled function algorithm, we apply it to a set of unconstrained global optimization problems. The data derived by numerical implementation shows that the proposed filled function can be used as an alternative algorithm when solving unconstrained global optimization problems.

• 조명전기설비학회논문지
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• 제17권5호
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• pp.88-93
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• 2003

본 논문에서는 반복법에 의한 쌍선형 시스템의 제어기 설계방법을 제안한다. 보조수열을 갖는 반복과정은 쌍선형 시스템으로부터 선형시변 시스템을 구성하는 과정에서 정의되고, 최적의 2차 가격함수를 갖는 쌍선형 시스템에서 궤환 제어기를 설계하기 위한 최적화 과정이 Riccati 접근법과 관련된 표현에 의해 주어진다. 제안된 방법에 의해서 개선된 성능과 우수한 수렴성이 성취됨을 시뮬레이션 결과에서 보인다.

• Journal of Electrical Engineering and Technology
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• 제4권2호
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• pp.240-248
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• 2009

An approach to control the position for an interior permanent magnet synchronous motor (IPMSM) based on an adaptive integral binary observer is described. The binary controller with a binary observer is composed of a main loop regulator and an auxiliary loop regulator. One of its key features is that it alleviates chatter in the constant boundary layer. However, steady state estimation accuracy and robustness are dependent upon the thickness of the constant boundary layer. In order to improve the steady state performance of the binary observer and eliminate the chattering problem of the constant boundary layer, a new binary observer is formed by adding extra integral dynamics to the existing switching hyperplane equation. Also, the proposed adaptive integral binary observer applies an adaptive scheme because the parameters of the dynamic equations such as the machine inertia and the viscosity friction coefficient are not well known. Furthermore, these values can typically be easily changed during normal operation. However, the proposed observer can overcome the problems caused by using the dynamic equations, and the rotor position estimation is constructed by integrating the rotor speed estimated with a Lyapunov function. Experimental results obtained using the proposed algorithm are presented to demonstrate the effectiveness of the approach.

• 전기학회논문지
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• 제56권1호
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• pp.71-80
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• 2007

A control approach for the sensorless speed control of interior permanent magnet synchronous motor(IPMSM) based on adaptive integral the binary is proposed. With a main loop regulator and an auxiliary loop regulator, the binary observer has a property of the chattering alleviation in the constant boundary layer. However, the width of the constant boundary limits the steady state estimation accuracy and robustness. In order to improve the steady state performance of the binary observer, the binary observer is formed by adding extra integral augmented switching the hyperplane equation. By mean of integral characteristics, the rotor speed can be finely estimated and utilized for a sensorless speed controller for IPMSM. The proposed adaptive integral binary observer applies an adaptive scheme, because the parameters of the dynamic equations such as the machine inertia or the viscosity friction coefficient is not well known and these values can be easily changed generally during normal operation. Therefore, the observer can overcome the problem caused by using the dynamic equations, and the rotor speed estimation is constructed by using the Lyapunov function. The experimental results of the proposed algorithm are presented to demonstrate the effectiveness of the approach.

• 제어로봇시스템학회논문지
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• 제18권10호
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• pp.902-911
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• 2012

This paper proposes a robust adaptive fuzzy backstepping control scheme for trajectory tracking of an electrically driven nonholonomic mobile robot with uncertainties and actuator dynamics. A complete model of an electrically driven nonholonomic mobile robot described in this work includes all models of the uncertain robot kinematics with a nonholonomic constraint, the uncertain robot body dynamics with uncertain frictions and unmodeled disturbances, and the uncertain actuator dynamics with disturbances. The proposed control scheme uses the backstepping control approach through a kinematic controller and a robust adaptive fuzzy velocity tracking controller. The presented control scheme has a voltage control input with an auxiliary current control input rather than a torque control input. It has two FBFNs(Fuzzy Basis Function Networks) to approximate two unknown nonlinear robot dynamic functions and a robust adaptive control input with the proposed adaptive laws to overcome the uncertainties such as parameter uncertainties and external disturbances. The proposed control scheme does not a priori require the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. It can also alleviate the chattering of the control input. Using the Lyapunov stability theory, the stability of the closed-loop robot control system is guaranteed. Simulation results show the validity and robustness of the proposed control scheme.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권3호
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• pp.152-163
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• 2004

This paper presents a sensorless speed control of cylindrical permanent magnet synchronous motors(PMSM) using an adaptive integral binary observer In view of composition with a main loop regulator and an auxiliary loop regulator, the binary observer has a property of the chattering alleviation in the constant boundary layer. However, the steady state estimation accuracy and robustness are dependent upon the width of the constant boundary. In order to improve the steady state performance of the binary observer, the binary observer is formed by adding extra integral dynamics to the switching hyperplane equation. With the help of integral characteristic, the rotor speed can be finely estimated and utilized for a sensorless speed controller for PMSM. Since the Parameters of the dynamic equations such as machine inertia or a viscosity friction coefficient are lot well known, there are many restrictions in the actual implementation. The proposed adaptive integral binary observer applies an adaptive scheme so that observer may overcome the problem caused by using the dynamic equations and the rotor speed is constructed by using the Lyapunov function. The observer structure and its design method are described. The experimental results of the proposed algorithm are presented to demonstrate the effectiveness of the approach.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제5B권1호
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• pp.70-77
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• 2005

This paper presents sensorless speed control of a cylindrical permanent magnet synchronous motor (PMSM) using the adaptive integral binary observer. In view of the composition with a main loop regulator and an auxiliary loop regulator, the normal binary observer has the feature of chattering alleviation in the constant boundary layer. However, the steady state estimation accuracy and robustness are dependent upon the thickness of the constant boundary layer. In order to improve the steady state performance of the binary observer, a new binary observer is formed by the addition of extra integral dynamics to the existing switching hyperplane equation. Also, because the parameters of the dynamic equations such as machine inertia or viscosity friction coefficient are not well known and these values can be changed during normal operations, there are many restrictions in the actual implementation. The proposed adaptive integral binary observer applies an adaptive scheme so that the observer may overcome the problems caused by using dynamic equations. The rotor speed is constructed by using the Lyapunov function. The observer structure and its design method are described. The experimental results of the proposed algorithm are presented to prove the effectiveness of the approach.