Abstract:

Aiming at the problem that noise increases the error probability of the transmission signals of nonlinear digital communication system， a multivariate communication system based on discrete Bidirectional Associative Memory （BAM） neural network was proposed. Firstly， the appropriate number of neurons and memory vectors were selected according to the signals to be transmitted， the weight matrix was calculated， and BAM neural network was generated. Secondly， the multivariate signals were mapped to the initial input vectors with modulation amplitude and continuously input into the system. The input was iterated through the neural network and Gaussian noise was added to each neuron. After that， the output was sampled according to the code element interval， and then transmitted in the lossless channel， and the decision was decoded by the receiver according to the decision rule. Finally， in the field of image processing， the proposed system was used to transmit the compressed image data and decode the recovered image. Simulation results show that for weakly modulated signals with large code element interval， with the increase of noise intensity， the error probability firstly decreases and then increases， and the stochastic resonance phenomenon is relatively obvious. At the same time， the error probability is positively correlated with the radix number of the signal， and negatively correlated with the signal amplitude， code element interval and the number of neurons. Under certain conditions， the error probability can reach 0. These results show that BAM neural network can improve the reliability of digital communication system through noise. In addition， the similarity of the image restored by decoding shows the improvement of moderate noise on image restoration effect， extending the application of BAM neural network and stochastic resonance in image compression coding.

Key words: Bidirectional Associative Memory (BAM) neural network, multivariate communication system, stochastic resonance, error probability, image compression

摘要：

针对噪声导致非线性数字通信系统传输信号的差错概率增加的问题，提出一种基于离散双向联想记忆（BAM）神经网络的多元通信系统。首先，根据需要传输的信号，选取适当的神经元数量和记忆向量，计算权值矩阵，并生成BAM神经网络；然后将多元信号映射为具有调制幅度的初始输入向量并不断输入系统，通过神经网络进行循环迭代，并向各神经元添加高斯噪声，之后按照码元间隔采样输出并在无损信道中传输，接收端依据判决规则译码判决；最后在图像处理领域，利用所提系统传输图像压缩后的数据并解码恢复图像。仿真结果表明，对于码元间隔较大的弱调制信号，随着噪声强度的增加，差错概率先减后增，随机共振现象比较明显；差错概率还与信号的进制数呈正相关关系，与信号幅度、码元间隔和神经元个数呈负相关关系，某些条件下，差错概率可以达到0。以上结果表明BAM神经网络可以通过噪声改善数字通信系统的可靠性。另外，解码恢复图像的相似度显示了适量噪声对图像恢复效果的改善，扩展了BAM神经网络和随机共振在图像压缩编码中的应用。

关键词: 双向联想记忆神经网络, 多元通信系统, 随机共振, 差错概率, 图像压缩