[1] 杨明华, 吴东亚, 董玉才,等. 大口径机枪加速寿命试验技术研究[C]∥亚太地区信息论学术会议. 杭州:中国电子学会信息论分会, 2011:360-363. YANG Ming-hua, WU Dong-ya, DONG Yu-cai, et al.The study on the accelerated life test technology of the large-caliber machine gun[C]∥The 2nd Asia-Pacific Conference on Information Theory. Hangzhou:Information Theory Society, Chinese Institute of Electronics, 2011:360-363.(in Chinese) [2] 张玲, 杨明华, 吴东亚,等. 基于加速寿命试验的大口径机枪寿命理论研究[C]∥亚太地区信息论学术会议. 杭州:中国电子学会信息论分会, 2011:86-89. ZHANG Ling, YANG Ming-hua, WU Dong-ya, et al. Research on thetechnology of accelerated life test of the large-caliber machine gun[C]∥The 2nd Asia-Pacific Conference on Information Theory. Hangzhou:Information Theory Society, Chinese Institute of Electronics, 2011:86-89.(in Chinese) [3] 陈国利, 韩海波, 于东鹏. BP神经网络的身管寿命预测方法[J]. 火力与指挥控制, 2008, 33(9):146-148. CHEN Guo-li, HAN Hai-bo, YU Dong-peng.Prediction method of barrels' life based on BP neural network[J]. Fire Control and Command Control, 2008, 33(9):146-149.(in Chinese) [4] 张军, 单永海, 曹殿广,等. 基于最小二乘支持向量机的机枪加速寿命建模[J]. 兵工学报, 2012, 33(1):63-68. ZHANG Jun, SHAN Yong-hai,CAO Dian-guang, et al. Accelerated life modeling for machine gun based on LS- SVM[J]. Acta Armamentarii, 2012, 33(1):63-68.(in Chinese) [5] 单永海, 张军, 王全正,等. 机枪身管常温综合寿命试验技术研究[J]. 兵工学报, 2013, 34(1):1-7. SHAN Yong-hai, ZHANG Jun, WANG Quan-zheng, et al.Study on lifetime test for machinegun barrel in normal temperature[J]. Acta Armamentarii, 2013, 34(1):1-7.(in Chinese) [6] 方峻, 吴华晴. 融合理论退化模拟与试验数据的身管寿命预测[J]. 机械科学与技术, 2014, 33(10):1468-1472. FANG Jun,WU Hua-qing. Prediction of the barrel life based on the theoretical degradation simulation and the experimental data[J]. Mechanical Science and Technology for Aerospace Engineering, 2014, 33(10):1468-1472.(in Chinese) [7] Specht D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6):568-576. [8] vün Polat, Tülay Yldrm. Genetic optimization of GRNN for pattern recognition without feature extraction[J]. Expert Systems with Applications, 2008, 34(4):2444-2448. [9] Xia C H, Lei B J, Wang H P, et al. GRNN short-term load forecasting model and virtual instrument design[J]. Energy Procedia, 2011, 13:9150-9158.
[10] 潘文超. 应用果蝇优化算法优化广义回归神经网络进行企业经营绩效评估[J]. 太原理工大学学报:社会科学版, 2011, 29(4):1-5. PAN Wen-chao.Using fruit fly optimization algorithm optimized general regression neural network to construct the operating performance of enterprises model[J]. Journal of Taiyuan University of Technology: Social Science Edition, 2011, 29(4):1-5.(in Chinese) [11] 王海军, 涂凯, 闫晓荣. 基于果蝇优化算法的GRNN模型在边坡稳定预测中的应用[J]. 水电能源科学, 2015, 33(1):124-126. WANG Hai-jun, TU Kai, YAN Xiao-rong. Application of general regression neural network to predict slope stability based on fruit fly optimization algorithm[J]. Water Resources and Power, 2015, 33(1):124-126.(in Chinese) [12] 王英博,聂娜娜,王铭泽,等. 修正型果蝇算法优化GRNN网络的尾矿库安全预测[J]. 计算机工程, 2015,41(4):267-272. WANG Ying-bo, NIE Na-na, WANG Ming-ze, et al. Mine tailings facilities safety evaluation of GRNN optimized by modified fruit fly algorithm[J]. Computer Engineering, 2015,41(4):267- 272. (in Chinese) [13] 张燕君, 刘文哲, 付兴虎, 等.基于自适应变异果蝇优化算法和广义回归神经网络的布里渊散射谱特征提取[J]. 光谱学与光谱分析, 2015,35(10):2916-2923. ZHANG Yan-jun, LIU Wen-zhe, FU Xing-hu, et al. A Brillouin scattering spectrum feature extraction based on flies optimization algorithm with adaptive mutation and generalized regression neural network[J]. Spectroscopy and Spectral Analysis, 2015,35(10): 2916-2923. (in Chinese) [14] Pan W C. A new fruit fly optimization algorithm: taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012, 26(2):69-74.
第38卷 第1期2017 年1月兵工学报ACTA ARMAMENTARIIVol.38No.1Jan.2017
|