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The Analysis and Design of the Reliable Iterative Decoding Algorithm based on Uncertain Reasoning

The Analysis and Design of the Reliable Iterative Decoding Algorithm based on Uncertain Reasoning
基于不确定推理的可靠迭代译码算法分析与设计
批准号:
12650400
负责人:
MATSUSHIMA Toshiyasu
金额:
$2.24万
依托单位:
依托单位国家:
日本
项目类别:
Grant-in-Aid for Scientific Research (C)
财政年份:
2000
资助国家:
日本
项目状态:
已结题
起止时间:
2000 至 2002

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中文摘要
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英文摘要
1. We analyzed and generalized our original reasoning algorithm, which is already proposed, based on both Bayesian Network and Graphical Model. Then, we designed a new algorithm for calculating posterior probability on probability models including classes of efficient codes. We also inspected the algorithm in terms of both theoretical and simulating points and then got good results.2. Moreover, we inspected both convergence and accuracy of the algorithm in terms of the differential geometry, machine learning and statistics and then we gave theoretical understandings onto it.3. We defined the generalized posterior probability distribution and made mathematical expression of a problem of the uncertain reasoning given the observed probability distribution. It turned out that the result of our reasoning method has a lot of mathematical meanings that is very important. We improved our algorithm by reducing both the calculation and memory occupation, and by paralyzing its procedures.4. We ap … More plied our algorithm on the Extended Junction Graph, which is also our generalized original graph, then proposed the efficient parallel belief propagation algorithm. As a typical application, we applied our algorithm, which is already mentioned in 2., for decoding on Extended Junction Graph constructed by LDPC codes. We examined its natures in terms of both theoretical and experimental aspects.5. We used our Extended Junction Graph for both convolution codes and tail biting codes, then we applied our algorithm mentioned in 2. for decoding both codes. We examined its results from the both mathematical and computational points of view.6. We applied our generalized algorithm for calculating posterior probability, which is already proposed year 2001, to a special model class that has guarantee of convergence. The whole procedure guarantees mathematical meanings as well as both efficient calculation and memory occupation.7. We developed out results in year 2001 on decoding algorithm, then inspected from point of theoretical and experimental view. Less
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松嶋敏泰: "情報論的学習理論における数理モデル"人工知能学会誌. Vol.16,No.2. 252-255 (2001)
Toshiyasu Matsushima:“基于信息的学习理论中的数学模型”,人工智能学会杂志,第 16 卷,第 252-255 期(2001 年)。
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通讯作者:
野村亮: "メモリ量を低減した近似ベイズ符号化アルゴリズム"電子情報通信学会論文誌A. Vol.85-A, No.5. 46-59 (2003)
Ryo Nomura:“减少内存量的近似贝叶斯编码算法”IEICE Transactions A. Vol.85-A,No.5 (2003)。
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Yoshifumi Ukita, Toshiyasu Matsushima, and Shigeichi Hirasawa: "On Learning Boolean Function using Fourier Transformation"Technical Report of The Institute of Electronics, Information and Communication Engineers. (COMP2000-56). 49-55 (2000)
Yoshifumi Ukita、Toshiyasu Matsushima 和平泽茂一:“On Learning Boolean Function using Fourier Transformation”电子信息通信工程师学会的技术报告。
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