机载武器中光电目标探测器故障诊断研究

Jun DENG,Yue-wen ZHOU,Hai-fang SONG,Ya-wei GE

Computer Simulation（2015）

Cited 1|Views12

Abstract

在机载武器中,光电目标探测器的故障诊断对机载武器系统作战效能的提高尤为重要.由于提供的测试信号有限,同时信号之间存在非线性关系,导致确定故障的位置和原因非常困难,诊断准确率不高.而传统故障诊断方法存在局限性,诊断时间长.针对以上问题,提出了一种结构优化模拟退火粒子群神经网络方法.首先,将光电目标探测器的采集数据预处理后训练网络,利用模拟退火粒子群算法调整权值.然后,根据神经元间的相对重要度和等效连接关系,优化连接结构,再引入神经元增益建立准则优化隐节点数.最后,利用训练后的神经网络完成光电目标探测器的故障诊断.实验结果表明,新方法能够有效地提高诊断准确率.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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