• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.65-72
• /
• 2007

In this study, we used nondestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of weld flaws. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from weld flaws in time domain. Through this process, we compared advantages/ disadvantages of two algorithms and confirmed application methods of two algorithms.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.315-319
• /
• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.6 no.3
• /
• pp.182-188
• /
• 2006

An artificial neural network model for the microwave characteristics of an InGaAs/InP hemt for 70 nm gate length has been developed. The small-signal microwave parameters have been evaluated to determine the transconductance and drain-conductance. We have further investigated the frequency characteristics of the device. The neural network training have been done using the three layer architecture using Levenberg-Marqaurdt Backpropagation algorithm. The results have been compared with the experimental data, which shows a close agreement and the validity of our proposed model.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.11
• /
• pp.124-131
• /
• 2001

Fault diagnosis of an assembled small motor relies usually on human experts hearing ability. The quality of diagnosis depends, however, heavily on physical conditions of the human experts. A fault diagnosis system for assembled small motors is developed using artificial neural network (ANN) in this paper. It is consisted of sound sampling device and fault diagnosis software package. Six parameters are defined to characterize the sampled sound waves. The Levenberg-Marquardt Backpropagation (LMBP) Algorithm is used to diagnose the fault of assembled small motors. Experimental results for more than two hundred small motors verify the performance of the developed system.

• Journal of the Korean Society of Safety
• /
• v.16 no.4
• /
• pp.58-64
• /
• 2001

In this study, it is developed that the system for effective prediction of residual stresses by the back-propagation algorithm using the neural network. To achieve This goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm. This system can no only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Structural Engineering and Mechanics
• /
• v.45 no.6
• /
• pp.779-802
• /
• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.6
• /
• pp.89-95
• /
• 2000

In order to achieve effective prediction of residual stresses, the series experiment were carried out and the residual stresses were measured using the backgpropagation algorithm from the neural network and the sectional method. Using the experimental results, the optimal control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on residual stresses during GMA welding processes. The results obtained from the comparison between the measured and calculated results, showed that the neural network based on backpropagation algorithm can be sued in order to control weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also, improve the quantity control for welded structures. The development of the system is goal in this study.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.9
• /
• pp.739-746
• /
• 1991

This paper develops a modified multilayer perceptron architecture which speeds up learning as well as the net's mapping accuracy. In Phase I, a cluster partitioning algorithm like the Kohonen's self-organizing feature map or the leader clustering algorithm is used as the front end that determines the cluster to which the input data belongs. In Phase II, this cluster selects a subset of the hidden layer nodes that combines the input and outputs nodes into a subnet of the full scale backpropagation network. The proposed net has been applied to two mapping problems, one rather smooth and the other highly nonlinear. Namely, the inverse kinematic problem for a 3-link robot manipulator and the 5-bit parity mapping have been chosen as examples. The results demonstrate the proposed net's superior accuracy and convergence properties over the original backpropagation network or its existing improvement techniques.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1730-1731
• /
• 2007

In this paper, we present a direct adaptive control method using neural networks for the control of nonlinear systems. The weights of neural networks are trained by an adaptive backpropagation algorithm based on Lyapunov stability theory. We develop the parameter update-laws using the neural network input and the error between the desired output and the output of nonlinear plant to update the weights of a neural network in the sense that Lyapunove stability theory. Beside the output tracking error is asymptotically converged to zero.

• Journal of Korean Society of Water and Wastewater
• /
• v.12 no.4
• /
• pp.43-52
• /
• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.