• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.509-517
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• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.228-228
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• 2000

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 an 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 obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• Proceedings of the KOR-KST Conference
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• 1999.10a
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• pp.446-451
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• 1999

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.7-15
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• 2005

Numerical analysis model 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 is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) 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 of 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.48-54
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• 2002

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules are created by an expert. To adapt the more precise model is implemented by error back-propagation learning algorithm to adjust the link-weight of fuzzy membership function in the neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of the proposed algorithm designed above, an operating characteristic of a DC servo motor with variable load is investigated.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.252-257
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• 2002

We designed a neuro-fuzzy controller to improve some problems that are happened when the DC servo motor is controlled by a PID controller or a fuzzy logic controller. Our model proposed in this paper has the stable and accurate responses, and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, the proposed controller is applied to the speed control of DC servo motor. The results showed that the proposed controller did not produce the overshoot, which happens when PID controller is used, and also it did not produce the steady state error when FLC is used. And also, it reduced the settling time about 10%. In addition, we could by aware that our model was only about 60% of the value of current peak of PID controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• Smart Structures and Systems
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• v.15 no.4
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• pp.931-947
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• 2015

This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.