• International Journal of Control, Automation, and Systems
• /
• 제4권5호
• /
• pp.545-558
• /
• 2006

In this paper, PD-like visual servoing is modified in two ways: a sliding-mode observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate the unknown gravity and friction. Based on Lyapunov method and input--to-state stability theory, we prove that PD-like visual servoing with the sliding mode observer and the neuro compensator is robust stable when the gain of the PD controller is bigger than the upper bounds of the uncertainties. Several simulations are presented to support the theory results.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.1865-1869
• /
• 2004

A new model for the propagation prediction for mobile communication network inside building is presented in this paper. The model is based on the determination of the dominant paths between the transmitter and the receiver. The field strength is predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved.

• 한국지능시스템학회논문지
• /
• 제8권6호
• /
• pp.58-69
• /
• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• 전기학회논문지P
• /
• 제63권3호
• /
• pp.189-194
• /
• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2000년도 추계종합학술대회
• /
• pp.642-646
• /
• 2000

The use of neural-fuzzy system to model mesh seam welding is described in this paper. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters affecting quality include the arc voltage, the welding current torch travel speed and the pressure and so on. The relationship between the welding parameters and weld quality is not a direct one, md' in addition, the effect of the weld parameter variables are not independent of the each other. The effectiveness of the proposed neuro-fuzzy algorithms is demonstrated by computer simulations.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.123.2-123
• /
• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2002년도 ICCAS
• /
• pp.110.1-110
• /
• 2002

We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 1999년도 학술대회논문집-국제 전기방전 및 플라즈마 심포지엄 Proceedings of 1999 KIIEE Annual Conference-International Symposium of Electrical Discharge and Plasma
• /
• pp.279-284
• /
• 1999

Although the switched reluctance motor (SRM) has a several advantages such as simple magnetic structure, robustness, wide range of speed characteristics and simple driving, it has a considerable inherent torque ripple and speed variation duet to the driving characteristics of pulse current waveform and the nonlinear inductance profile. The high torque ripple and speed variation inhibits wide application. The minimization of the torque ripple is very important in high performance servo drive applications, which require smooth operation with minimum torque pulsations. This paper presents the new SRM torque modeling technique for the control of instantaneous torque. The SRM is modeled by the database of torque profiles for every small variation in currents and rotor angles, which is inferred from the several measured data by the adaptive neuro-fuzzy inference technique. Simulation results demonstrating the effectiveness of proposed torque modeling technique are presented.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
• /
• pp.420-424
• /
• 1998

A new type of controller for DC-DC converters is presented. The proposed neuro-fuzzy controller combines fuzzy logic with neural networks to adjust parameters of the fuzzy controller to the most appropriate. Neither the exact mathematical models of the DC-DC converters nor the tuning process of the parameters of the fuzzy controller are needed in the proposed scheme. Simulation results are presented to show the above process and transient, steady state responses, and load regulation of the given system.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 D
• /
• pp.2120-2122
• /
• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.