• Journal of Advanced Marine Engineering and Technology
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• v.21 no.1
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.39-44
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• 1998

Determining the absolute location of a mobile robot is essential in the navigation of a mobile robot. In this paper, a method to determine the position of a mobile robot through the visual image of a landrnark using neural networks is proposed. In determining the position of a mobile robot on the world coordinate, there is a position error because of uncertainty in pixels, incorrect camera calibration and lens distortion. To reduce the errors, a method using a BPNN(Back Propagation Neural Network) is proposed. The experimental results are presented to illustrate the superiority of the proposed method when comparing with the conventional methods.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.695-700
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• 2008

Artificial neural network (ANN) has been extensively used in areas of nonlinear system modeling, analysis and design applications. Basically, ANN has its distinct capabilities of implementing system identification and/or function approximation using a number of input/output patterns that can be obtained via numerical and/or experimental manners. The paper describes a role of ANN, especially a back-propagation neural network (BPN) in the context of engineering analysis, design and optimization. Fundamental mechanism of BPN is briefly summarized in terms of training procedure and function approximation. The BPN based causality analysis (CA) is further discussed to realize the problem decomposition in the context of multidisciplinary design optimization. Such CA is also applied to quantitatively evaluate the uncoupled or decoupled design matrix in the context of axiomatic design with the independence axiom.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.103-108
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• 2007

Neural network is used in many fields of control systems currently. However, It is not easy to obtain input-output pattern when neural network is used for the system of a single feedback controller and it is difficult to get satisfied performance with neural network when load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object in place of activation function of Neural Network output node. As the Neural Network self adaptive control system is designed in simple structure neural network input-output pattern problem is solved naturally and real tin Loaming becomes possible through general back propagation algorithm. The effect of the proposed Neural Network self adaptive control algorithm was verified in a test of controlling the speed of a A.C. servo motor equipped with a high speed computing capable DSP (TMS320C32) on which the proposed algorithm was loaded.

• The Journal of Information Technology
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• v.5 no.2
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• pp.55-67
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• 2002

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and varies the number of hidden layer node. This algorithm is expected to escaping from the local minimum and make the best environment for convergence to be change the number of hidden layer node. On the simulation tested this algorithm on two learning pattern. One was exclusive-OR learning and the other was $7{\times}5$ dot alphabetic font learning. In both examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in alphabetic font learning, the neural network enhanced to learning efficient about 41.56%~58.28% for the conventional back propagation. and HNAD(Hidden Node Adding and Deleting) algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.759-763
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.26-31
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• 2014

In this paper, we present performance analysis of video smoke detection based on BPN-Network that is using multi-smoke feature, and Neural Network. Conventional smoke detection method consist of simple or mixed functions using color, temporal, spatial characteristics. However, most of all, they don't consider the early fire conditions. In this paper, we analysis the smoke color and motion characteristics, and revised distinguish the candidate smoke region. Smoke diffusion, transparency and shape features are used for detection stage. Then it apply the BPN-Network (Back-Propagation Neural Network). The simulation results showed 91.31% accuracy and 2.62% of false detection rate.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.63-70
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• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

• Korean Management Science Review
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• v.9 no.1
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• pp.17-30
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• 1992

This paper is organized as follows. Section 2 illustrates several applications of neural networks. Section 3 presents the theoretical aspects of the major neural network paradigms as well as the structure of the back -propagation network used in the study. Section 4 describes the experiment including data analysis, modeling, and the performance criteria followed by the detailed discussion of the experimental results. Future research avenues including advantages and limitations of neural network are presented in the last section.

• Korean Journal of Biological Psychiatry
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• v.4 no.2
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• pp.194-197
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• 1997

Neural network models, also known as connectionist models or PDP models, simulate some functions of the brain and may promise to give insight in understanding the cognitive brain functions. The models composed of neuron-like elements that are linked into circuits can learn and adapt to its environment in a trial and error fashion. In this article, the history and principles of the neural network modeling are briefly reviewed, and its applications to psychiatry are discussed.