• Proceedings of the KIPE Conference
• /
• 2001.10a
• /
• pp.360-364
• /
• 2001

In this paper, a novel method of reaching law variable structure control based on fuzzy rules is present, which is that the reaching law parameters is on-line adjusted by fuzzy rules. This method is used in a digital ac position servo system, the experiment results show that the system designed by this method has both satisfactory quality and very smaller chattering.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.433-439
• /
• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.555-565
• /
• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.4
• /
• pp.279-286
• /
• 1996

In this paper, variable structure control(VSC) based on reaching law method with fuzzy inference for nonlinear systems is proposed. The reaching law means the reaching condition which forces an initial state of system to reach switching surface in finite time, and specifies the dynamics of a desired switching function. Since the conventional reaching law has fixed coefficients, the chattering can be existed largely in sliding mode. In the design of a proposed fuzzy reaching law, we fuzzify RP(representative point)'s orthogonal distance to switching surface and RP's distance the origin of the 2-dimensional space whose coordinates are the error and the error rate. The coefficients of the reaching law are varied appropriately by the fuzzy inference. Hence the state of system in reaching mode reaches fastly switching surface by the large values of reaching coefficients and the chattering is reduced in sliding mode by the small values of those. And the effectiveness of the proposed fuzzy reaching law method is showen by the simulation results of the control of a two link robot manipulator.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2003.10a
• /
• pp.103-114
• /
• 2003

This paper aims to propose a method that helps maintenance engineers to evaluate the damage states of bridge structure systems by using a Fuzzy Fault Tree Analysis. It may be stated that Fuzzy Fault Tree Analysis may be very useful for the systematic and rational fuzzy reliability assessment for real bridge structure systems problems because the approach is able to effectively deal with all the related bridge structural element damages in terms of the linguistic variables that incorporate systematically experts experiences and subjective judgement. This paper considers these uncertainties by providing a fuzzy reliability-based framework and shows that the identification of the optimum maintenance scenario is a straightforward process. This is achieved by using a computer program for LIFETIME. This program can consider the effects of various types of actions on the fuzzy reliability index profile of a deteriorating structures. Only the effect of maintenance interventions is considered in this study. However. any environmental or mechanical action affecting the fuzzy reliability index profile can be considered in LIFETIME. Numerical examples of deteriorating bridges are presented to illustrate the capability of the proposed approach. Further development and implementation of this approach are recommended for future research.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.120-123
• /
• 1997

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2005.04a
• /
• pp.201-208
• /
• 2005

Neuro-Fuzzy modeling approach is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system consists of friction pendulum systems (FPS) and a magnetorheological (MR) damper. Fuzzy model of the M damper is trained by ANFIS using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses or experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.10
• /
• pp.77-85
• /
• 1993

In this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases. Moreover, the relational structure adopted in the proposed neurocomputational approach can work in a highly parallel manner so that real-time applications of fuzzy sets are possibles as in fuzzy logic controllers, knowledge-based systems, and pattern recognition systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.45-49
• /
• 1997

In this paper, we controlled a Rotary Inverted Pendulum System using Neuro-Fuzzy Controller(NFC). The inverted pendulum system is widely used as a typical example of an unstable nonlinear control system which is difficult to control. Fuzzy theory have been because membership functions and rules of a fuzzy controller are often given by experts or a fuzzy logic control system. This controller is a feedforward multilayered network which integrates the basic elements and functions of a tradtional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such NFC can be constructed from training examples by learning rule, and the structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Using this controller, we presented the results that controlled a Rotary Inverted Pendulum System and the associated algorithms.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.720-723
• /
• 1997

The characteristics of ball and beam system using fuzzy inference technique can be described by fuzzy modeling. Therefore, this paper introduces a technique for fuzzy structure identification of nonlinear Input-output relation- ship using an adaptive fuzzy inference system. And the simulation result using adaptive fuzzy inference technique shows its effectiveness for fuzzy structure identification of nonlinear system.