• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.372-372
• /
• 2000

In this paper, we propose Neuro Fuzzy Polynomial Networks(NFPN) based on Polynomial Neural Network(PNN) and Neuro-Fuzzy(NF) for model identification of complex and nonlinear systems. The proposed NFPN is generated from the mutually combined structure of both NF and PNN. The one and the other are considered as the premise part and consequence part of NFPN structure respectively. As the premise part of NFPN, NF uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. As the consequence part of NFPN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. NFPN is available effectively for multi-input variables and high-order polynomial according to the combination of NF with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. In order to evaluate the performance of proposed models, we use the nonlinear function. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously.

• 대한전기학회논문지:전력기술부문A
• /
• 제52권8호
• /
• pp.471-480
• /
• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• 대한전기학회논문지:시스템및제어부문D
• /
• 제51권12호
• /
• pp.571-576
• /
• 2002

This paper is to develop jumping ring system with three sensor arrays and to control levitated ring using dynamic neural mode. Placing an aluminum ring on the core and switching on an AC source causes the ring to jump in the air due to induced currents. The educational system is composed of 40th optical sensor array, encode circuit, 89C51 microprocessor and control board. The control board consists of power IC, and phase controller. Real time process is present to obtain a height of levitated ring for three different sensor arrays. Based on the educational system and the proposed dynamic neural mode, the height of levitation of the ring is controlled by reference signals. This paper focuses on real system controls using the dynamic neural mode with on line learning algorithm.

• 한국공작기계학회논문집
• /
• 제11권4호
• /
• pp.62-67
• /
• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized loaming architecture. It is Proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tucking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 한국정밀공학회지
• /
• 제15권12호
• /
• pp.106-112
• /
• 1998

A major problem in automation of turning operations is the difficulty in obtaining a sufficient and reliable chip control. The chip should be detected in order to provide a optimum chip control for unmanned turning operation. Using the difference of energy radiated from the chip, chip Patterns are estimated using pyrometer. From the initial output from the pyrometer, chips are identified according to the backpropagation algorithm developed in the research. The learning system developed in this work can be applied in real-time control of turning process with minor modification in drive system.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
• /
• pp.2900-2902
• /
• 2005

본 논문에서는 환경과 시스템의 상호작용을 통한 경험에 의해 습득된 정보를 개체간 네트워크를 통하여 갱신하는 과정을 구성하는 연구를 하였다. 기존의 연구에서는 강화학습 알고리즘을 이용하여 임의의 구역에 대한 지도 정보를 습득하고 이를 바탕으로 개체들 각각의 최적의 행동 정책을 구성하는 바 이 때 각각의 체개체가 가지고 있는 최단경로에 대한 정보의 우위를 결정하는 과정을 추가하였다. 이를 바탕으로 최종적으로 선택된 경로에 대한 정보를 업데이트하여 구성 된 네트워크를 통한 개체간 데이터를 동시에 공유하는 과정을 거쳐서 각각의 시스템이 스스로 정보를 갱신하는 방법을 제안하였다 또한 이 제안한 개념의 적합성을 입증하기 위하여 개체간의 정보를 통합하고 비교하는 실험을 수행하여 성공적인 결과를 얻었다.

• 한국정밀공학회지
• /
• 제15권4호
• /
• pp.15-26
• /
• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• 제어로봇시스템학회논문지
• /
• 제7권6호
• /
• pp.509-517
• /
• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• 제어로봇시스템학회논문지
• /
• 제6권3호
• /
• pp.256-262
• /
• 2000

This paper proposes a new sliding mode controller combined with a multi-layer neural network using the error back propagation learning algorithm,, The network acts as a compensator of the conventional sliding mode controller to improve the control performance when initial assumptions of uncertainty bounds of system parameters are violated. The proposed controller can reduce th steady state error of conventional sliding mode controller with the boundary layer technique Computer simulation results show that the proposed method is effective to control dynamic systems with unexpectably large uncertainties.

• 한국산업융합학회 논문집
• /
• 제7권2호
• /
• pp.167-172
• /
• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.