• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.983-987
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• 2003

Vibration causes noise and makes structure unstable. Especially, due to the effort of lightening, deformation of flexible structure is increased in its shape. Just a little disturbance causes vibration and low damping ratio causes residual vibration lasts long time. In this paper, by using a neuro-controller, which is one of the algorithm of adaptive control. we performed adaptive control of flexible cantilever plate and opened box structure with piezoelectric materials. The proposed adaptive vibration control algorithm, a neuro-controller, is proved in its effectiveness by applying to a opened box structure. The neuro-controller was implemented with DSP, and the real-time adaptive vibration control experiment results confirm that neuro-controller is reliable.

• 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.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.122-127
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• 1998

This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

• The Korean Journal of Pain
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• v.8 no.2
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• pp.363-366
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• 1995

Blockade of the ganglion impar was performed as an alternertive means of managing intractable neoplastic perineal pain of sympathetic origin. We successively treated a patient who had suffered from excessive perianal sweating with ganglion impar block using pure alcohol. Eight months after block, the patient has no complaint of perianal sweating. Ganglion impar block is an effective method in the treatment of excessive perianal sweating as well as perineal pain of sympathetic origin.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.70-70
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• 2000

Recently, a neuro-fuzzy approach, a combination of neural networks and fuzzy reasoning, has been playing an important role in the motor control. In this paper, a novel method of fiction compensation using neuro-fuzzy architecture has been shown to significantly improve the performance of a DC motor system with nonlinear friction characteristics. The structure of the controller is the neuro-fuzzy network with the TS(Takagi-Sugeno) model. A back-propagation neural network based on a gradient descent algorithm is employed, and all of its parameters can be on-line trained. The performance of the proposed controller is compared with both a conventional neuro-controller and a PI controller.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2283-2285
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• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.52-59
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• 1998

This paper represents identification and control designs using neural networks for a class of nonlinear dynamic systems. A proposed neuro-controller is a combination of a linear controller and a neural network, and is trained by indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based bar system, and is applicable to many dc-motor-driven precisiion servo mechanisms. The ideas, algorithm, and experimental results are described. Experimental resutls are shown to be superior to those of conventional control.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.