• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2528-2530
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• 2004

Neural network minimization problems are often conditioned and in this contribution way to handle this will be discussed. It is shown that a better conditioned minimization problem can be obtained if the problem is separated with respect to the linear parameters. This will increase the convergence speed of the minimization. One of the most powerful uses of neural networks is in function approximation(curve fitting)[1]. A main characteristic of this solution is that function (f) to be approximated is given not explicitly but implicitly through a set of input-output pairs, named as training set, that can be easily obtained from calibration data of the measurement system. In this context, the usage of Neural Network(NN) techniques for modeling the systems behavior can provide lower interpolation errors when compared with classical methods like polynomial interpolation. This paper solve of single-variable minimization using neural network.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.3-5
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• 1993

This paper presents a identification techniques of coherent generators for dynamic equivalents using artificial neural networks. In the developed neural network, inputs are the power system parameters which have a property of coherency. Outputs of the neural network are coherency and error indices which are derived from density measure concept. The learning of developed neural network is carried out by means of error back-propagation algorithm. Identification of coherent generators are implemented by proposed grouping algorithm using coherency and error indices. The proposed method is confirmed by simulations for 39-bus New England system.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.239-241
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• 1992

In this paper, back-propagation neural network is used for the identification and trajectory control of a DC motor. The neural network is trained to identify the unknown nonlinear dynamics of the motor and load and the trained neural network is used for speed control of the DC motor to have good performance. Simulation results show the good performance of the control system based on the neural network under arbitrarily chosen speed trajectories.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2137-2147
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• 2004

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end-of-life phase. Disposal products have the uncertainties of product status by usage influences during product use phase, and recycling cells are formed design, process and usage attributes. In order to deal with the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. Fuzzy C-mean algorithm and a heuristic approach based on fuzzy ART neural network is suggested. Especially, the modified Fuzzy ART neural network is shown that it has a good clustering results and gives an extension for systematically generating alternative solutions in the recycling cell formation problem. Disposal refrigerators are shown as examples.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.3
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• pp.57-62
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• 2005

Neural networks, which have learning and self-organizing abilities, can be advantageously used in the pattern recognition. Neural network techniques have been widely used in monitoring and diagnosis, and compare favourable with traditional statistical pattern recognition algorithms, heuristic rule-based approaches, and fuzzy logic approaches. In this study the fault diagnosis of the face-milling using the artificial neural network was investigated. After training, the sample which measure load current was monitored by constant output results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.11
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• pp.650-653
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• 2005

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.211-216
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• 2004

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.