神经网络技术在激光脉冲解码领域的应用研究

Laser pulse decoding based on neural network technology

为提高激光脉冲解码过程的准确性和识别效率,采用神经网络技术对激光脉冲编码解码进行了仿真研究。应用线性神经网络对有规律的编码,如周期型编码和等差型编码,进行了识别。仿真结果表明,对于PCM码,需要约2个周期的脉冲就可准确预测下一个脉冲到达的时间;对于等差型码,需要4个脉冲就可以达到精度要求。然后,应用概率神经网络对伪随机型编码的最小周期进行了识别。仿真结果表明,可以在信息量较少的情况下准确识别此类型编码的最小周期。

In order to improve the accuracy and recognition efficiency in the process of decoding,laser pulse code was decoded using neural network technology.Firstly,the regular code,such as periodic code and arithmetic code,was decoded using linear neural network.The results showed that,it can recognize PCM code accurately if the pulses of two periods were acquired,and only 4 pulses for arithmetic code.After that,the minimum period of pseudo-random code was recognized using PNN.The results showed that,PNN can identify the minimum period only in limited amount of information.