• Title, Summary, Keyword: BP algorithm

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An Improvement of UMP-BP Decoding Algorithm Using the Minimum Mean Square Error Linear Estimator

  • Kim, Nam-Shik;Kim, Jae-Bum;Park, Hyun-Cheol;Suh, Seung-Bum
    • ETRI Journal
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    • v.26 no.5
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    • pp.432-436
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    • 2004
  • In this paper, we propose the modified uniformly most powerful (UMP) belief-propagation (BP)-based decoding algorithm which utilizes multiplicative and additive factors to diminish the errors introduced by the approximation of the soft values given by a previously proposed UMP BP-based algorithm. This modified UMP BP-based algorithm shows better performance than that of the normalized UMP BP-based algorithm, i.e., it has an error performance closer to BP than that of the normalized UMP BP-based algorithm on the additive white Gaussian noise channel for low density parity check codes. Also, this algorithm has the same complexity in its implementation as the normalized UMP BP-based algorithm.

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Adaptive Error Constrained Backpropagation Algorithm (적응 오류 제약 Backpropagation 알고리즘)

  • 최수용;고균병;홍대식
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.10C
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    • pp.1007-1012
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    • 2003
  • In order to accelerate the convergence speed of the conventional BP algorithm, constrained optimization techniques are applied to the BP algorithm. First, the noise-constrained least mean square algorithm and the zero noise-constrained LMS algorithm are applied (designated the NCBP and ZNCBP algorithms, respectively). These methods involve an important assumption: the filter or the receiver in the NCBP algorithm must know the noise variance. By means of extension and generalization of these algorithms, the authors derive an adaptive error-constrained BP algorithm, in which the error variance is estimated. This is achieved by modifying the error function of the conventional BP algorithm using Lagrangian multipliers. The convergence speeds of the proposed algorithms are 20 to 30 times faster than those of the conventional BP algorithm, and are faster than or almost the same as that achieved with a conventional linear adaptive filter using an LMS algorithm.

An Efficient Decoding Algorithm of LDPC codes (LDPC 부호의 효율적인 복호 방법에 관한 연구)

  • Kim, Joon-Sung;Shin, Min-Ho;Song, Hong-Yeop
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.9C
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    • pp.1227-1234
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    • 2004
  • In this paper, we propose a modified Normalized-BP algorithm by changing the normalization factor according to the reliability of updated messages. Proposed algorithm has almost same decoding complexity as Normalized-BP algorithm and achieves a bit-error probability of $10^4$within 0.02dB away from compared with LLR-BP algorithm.

Real-time Hand Gesture Recognition System based on Vision for Intelligent Robot Control (지능로봇 제어를 위한 비전기반 실시간 수신호 인식 시스템)

  • Yang, Tae-Kyu;Seo, Yong-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.10
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    • pp.2180-2188
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    • 2009
  • This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

A Neural Network Combining a Competition Learning Model and BP ALgorithm for Data Mining (데이터 마이닝을 위한 경쟁학습모텔과 BP알고리즘을 결합한 하이브리드형 신경망)

  • 강문식;이상용
    • Journal of Information Technology Applications and Management
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    • v.9 no.2
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    • pp.1-16
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    • 2002
  • Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

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An Implementation of Non-invasive Blood Pressure System Using Variable Characteristic Ratio (모듈방식의 가정용 혈압 측정 시스템 구현)

  • 이종수;노영아;이상용;박종억;김영길
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.5 no.7
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    • pp.1263-1271
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    • 2001
  • There are two methods in blood pressure measurement ; Invasive methode and Non-invasive method. The Invasive methode can get the 띠cod pressure measurement but, patient feel uncomfortable. So Non-invasive methode used generally. The Oscillometric method is typical Non-Invasive method. This method is commonly used to measure BP in electric sphygmomanometer and has various algorithm. In this paper it is described about a algorithm, it controls, determinates the cuff pressure, and fillers the measured BP data. This system can interface with PC(personal computer) by RS-232 and save the measured data in PC. This system deflates the cuff pressure by Solenoid valve. The main algorithm are oscillometric and maximum amplitude algorithm(MAA). MAA has various measured oscillation, it depends on patient's age, height, weight and arm circumference size. In this paper proposed system can measure Systolic BP, Diastolic BP, and Mean BP using Interpolation, Auto Reinflation algorithm.

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A Comparative Study on Neural Network Algorithms for Partial Discharge Pattern Recognition (부분방전 패턴인식기법으로서의 Neural Network 알고리즘 비교 분석)

  • Lee, Ho-Keun;Kim, Jeong-Tae
    • Proceedings of the KIEE Conference
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    • pp.109-112
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    • 2004
  • In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

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Principal Feature Extraction on Image Data Using Neural Networks of Learning Algorithm Based on Steepest Descent and Dynamic tunneling (기울기하강과 동적터널링에 기반을 둔 학습알고리즘의 신경망을 이용한 영상데이터의 주요특징추출)

  • Jo, Yong-Hyeon
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.5
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    • pp.1393-1402
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    • 1999
  • This paper proposes an efficient principal feature extraction of the image data using neural networks of a new learning algorithm. The proposed learning algorithm is a backpropagation(BP) algorithm based on the steepest descent and dynamic tunneling. The BP algorithm based on the steepest descent is applied for high-speed optimization, and the BP algorithm based on the dynamic tunneling is also applied for global optimization. Converging to the local minimum by the BP algorithm of steepest descent, the new initial weights for escaping the local minimum is estimated by the BP algorithm of dynamic tunneling. The proposed algorithm has been applied to the 3 image data of 12${\times}$12pixels and the Lenna image of 128${\times}$128 pixels respectively. The simulation results shows that the proposed algorithm has better performances of the convergence and the feature extraction, in comparison with those using the Sanger method and the Foldiak method for single-layer neural networks and the BP algorithm for multilayer neural network.

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Human Face Recognition used Improved Back-Propagation (BP) Neural Network

  • Zhang, Ru-Yang;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.21 no.4
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    • pp.471-477
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    • 2018
  • As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

Compare the accuracy of stereo matching using belief propagation and area-based matching (Belief Propagation를 적용한 스테레오 정합과 영역 기반 정합 알고리즘의 정확성 비교)

  • Park, Jong-Il;Kim, Dong-Han;Eum, Nak-Woong;Lee, Kwang-Yeob
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.119-122
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    • 2011
  • The Stereo vision using belief propagation algorithm that has been studied recently yields good performance in disparity extraction. In this paper, BP algorithm is proved theoretically to high precision for a stereo matching algorithm. We derive disparity map from stereo image by using Belief Propagation (BP) algorithm and area-based matching algorithm. Two algorithms are compared using stereo images provided by Middlebury web site. Disparity map error rate decreased from 52.3% to 2.3%.

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