• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.426-431
• /
• 1998

We propose a fuzzy rule based method for structure preserving dimensionality reduction. This method selects a small representative sample and applies Sammon's method to project it. The input data points are then augmented by the corresponding projected(output) data points. The augmented data set thus obtained is clustered with the fuzzy c-means(FCM) clustering algorithm. Each cluster is then translated into a fuzzy rule for projection. Our rule based system is computationally very efficient compared to Sammon's method and is quite effective to project new points, i.e., it has good predictability.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.8
• /
• pp.2088-2100
• /
• 1994

Considerable attention has been given to controller designs that utilize the variable structure system theory in order to achieve robust tracking performance of robotic manipulators subjected to parameter variations and extraneous disturbances. However, the theory has not had wide spread acceptance in practical control engineering community due mainly to the worry of chattering which is inherently ever-existing in the variable structure system. This paper presents a novel type of fuzzy-sliding mode controller to alleviate the chattering problem. A sliding mode controller for robust robot control is firstly synthesized with an assumption that the imposed system uncertainties satisfy matching conditions so that certain deterministic performances can parameters and control rules are obtained from a relation between predetermined sliding surfaces and representative points in the error state space. A two degree-of-freedom robotic manipulator subjected to a variable payload and a torque disturbance is considered in order to demonstrate superior tracking performance accrued from the proposed methodology.

• Proceedings of the KIEE Conference
• /
• 2001.07b
• /
• pp.1182-1184
• /
• 2001

This paper presents a neural network based self-tuning fuzzy PID control scheme for induction motor speed control. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PID controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink is performed to verify the effectiveness of the proposed scheme.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.04a
• /
• pp.327-339
• /
• 2003

In this paper, a self-organizing fuzzy controller(SOFC) for the industrial robot manipulator with a actuator located at the base is studied A fuzzy login composed of linguistic conditional statements is employed by defining the relations of input-output variable of the controller, In the synthesis of a FLC, one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult SOFC is proposed for a hierarchical control structure consisting of basic level and high level that modify control rules. The proposed SOFC scheme is simple in structure, fast in computations and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for robot with low joints.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.5 s.98
• /
• pp.104-111
• /
• 1999

A semi-active suspension system for a vehicle using an Electrorheological Fluid damper has been studied. Apparent viscosity of ERF(Electrorheological Fluid) can be changed rapidly by applying electric field. The damping force of ER damper can be selectively controlled by employing electric field to the ER fluid domain. This paper deals with a two-degree-of-freedom suspension using the ER damper for a quarter car model. An intelligent control method using fuzzy control with genetic algorithm has been employed to control the damping force of the ER damper. The GA designs the optimal structure and performance of Fuzzy Net Controller having hybrid structure. The designed fuzzy net controller has been compared with the skyhook type controller for a quarter car model. The computer simulation results show that the semi-active suspension with ER damper has a good performance in the sense of ride quality with less vibration for ground vehicle.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.1
• /
• pp.32-42
• /
• 2001

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.12
• /
• pp.691-696
• /
• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2653-2655
• /
• 2001

In this paper, we proposed a method of modeling a system using Fuzzy Equalization(FE) and Genetic Algorithm(GA). The initial model is constructed using FE. The antecedent parameters and the rules in fuzzy logic are tuned by GA. The proposed system minimizes the modeling error and the size of structure. The process of building membership functions using PDF(Probability Density Function) and GA tunes the antecedent parameter and rules for minimizing the error and structure. The usefulness of proposed method is demonstrated by applying to Box-Jenkins furnace data.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.67.1-67
• /
• 2001

This paper presents a neural network based self-tuning fuzzy PI-PD control scheme for robust speed control of induction motor. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PI-PD controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small ...

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.7
• /
• pp.2377-2389
• /
• 2022

At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important. This paper focuses on the video quality assessment method using different fuzzy neural networks. Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect. Secondly, two fuzzy neural network models are used to build the objective assessment method. By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained. Meanwhile the advantages and disadvantages of the two models are analysed. Lastly, the proposed method is compared with many recent related assessment methods. This paper will give the experimental results and the detail of assessment process.