一种可用于非线性码译码神经网络模型研究
A NEURAL NETWORK FOR DECODING OF NONLINEAR CODES
-
摘要: 本文提出一种非线性码神经网络译码方案,在纠错能力范围内对满足码距特性的一般非线性码以零错误概率进行纠错译码,并在检错能力范围内检错。文中具体描述了神经网络模型构造、学习算法及其理论依据。最后通过非线性等重码的译码实例表明此方案的有效性及理论和应用价值。
-
关键词:
- 非线性码; 译码; 神经网络
Abstract: A decoding strategy for nonlinear codes using a neural network is presented. Within the capacity of error correction, it can correct and detect errors of general nonlinear codes which have some specific code distance with zero error probability. This paper describes structure of the neural network, learning algorithm and theory analysis. Finally, one decoding example: nonlinear constant weight code is demonstrated to prove the availability and values of theory and application. -
Macmilliams F J, et al. The Theory of Error-correcting Codes. New York: North-Holland, 1977.[2]Bruck J, et al. Neural networks error correcting codes and polynomials over the binary n cube IEEE Trans. on Inform. Theory, 1989, IT-35(4): 976-987.[3]杨义先,林须端. 编码密码学.北京: 人民邮电出版社,1992, 304-456.[4]]杨义先.人工神经网络能量函数与纠错码译码算法.模式识别与人工智能,1991, 4(3): 22-27.[5]Jin Fan, et al. A new class of nonlinear error control codes based on neural networks. JSJO, 1995, 3(2): 109-116.[6]许成谦,等.神经网络与非线性码最小加权距离译码算法.北京邮电大学学报,1994, 17(2): 23-26.[7][7][8]Espositom A, et al. A neural network for error correcting decoding of binary linear codes[J].Neural Networks.1994, 7(1):195-202[9]Lifang Li, et al. A modified learning rule of the neural network for error correcting decoding. Proc. of ICONIP95, Beijing: 1995: 562-565. -
计量
- 文章访问数: 2145
- HTML全文浏览量: 111
- PDF下载量: 482
- 被引次数: 0
下载:
