搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于原子核密度的核电荷半径新关系

焦宝宝

引用本文:
Citation:

基于原子核密度的核电荷半径新关系

焦宝宝

Nucleus density based new relationship of nuclear charge radius

Jiao Bao-Bao
PDF
HTML
导出引用
  • 从核质量出发系统地研究核电荷半径, 进而得到核电荷半径的计算值和预言值. 运用AME2020数据库结合CR2013数据库得到已知质量且已知半径的原子核 (Z, N ≥ 8) 884个, 计算得到884个原子核密度. 研究原子核密度得到常参数经验公式, 利用此经验公式结合AME2020数据库得到核电荷半径的计算值与实验值之间的均方根误差σ = 0.093 fm. 考虑到中子数对原子核密度的影响, 添加了中子因子1/N进行修正, 均方根误差减少为0.047 fm. 再添加中子壳层效应进行修正, 均方根误差减小至0.034 fm. 基于修正后的经验公式结合AME2020数据库得到Z, N ≥ 8的1573个核电荷半径预言值, 其中一些预言值与近些年测得实验值的对比结果说明利用此关系得到的预言值具有一定的意义. 此外, 剔除一些特殊的壳层后, 剩余791个核电荷半径常参数经验公式计算值的均方根误差为 σ = 0.063 fm, 修正后降至 σ = 0.032 fm. 研究结果表明本文提出的核电荷半径关系具有一定的简便性和可靠性, 可以与$ A^{1/3} $律和$ Z^{1/3} $律修正后的全局核电荷半径关系相媲美. 最后, 本文又引入Levenberg-Marquardt (L-M)算法训练前馈神经网络分别对884和791个原子核密度进行建模计算, 均方根误差分别为 0.018 fm 和 0.014 fm, 比修正后经验公式的误差减小约50%, 得到的预言值与近几年测得的实验值也较接近.
    In this paper we predict and evaluate the value of the nuclear charge radius by analyzing the relationship between nuclear mass and nuclear charge radius.We obtain 884 nuclei (Z, N ≥ 8) with known mass and known charge radii by combining AME2020 database with CR2013 database, and calculate the mass densities $ \rho_\text{m} $ of the 884 nuclei. We aim to obtain an empirical formula of one constant which is useful in describing and predicting nuclear charge radius. With the empirical formula and the AME2020 database, the root-mean-square deviation (RMSD) of the nuclear charge radius of $ \sigma = 0.093 $ fm is successfully obtained.Considering the influence of neutron numbers on $\rho_{\rm{m}}$, we use the neutron factor ${1}/{N} $ to correct the empirical formula, and the RMSD is reduced to σ = 0.047 fm (the accuracy is increased by about 50%). The second correction is shell effect of neutrons. The results show that the RMSD of nuclear charge radius is reduced to 0.034 fm based on shell effect of neutrons. We use the empirical formula with corrections to predict the nuclear charge radius (1573 nuclear charge radius with Z, N ≥ 8) which is difficult to measure experimentally. The difference between our predicted values based on AME2020 database and the experimental values measured in recent years is in the allowable range of deviation. The result shows that the new relation for nuclear charge radius is simple and reliable. In addition, the RMSD of the calculation value for 791 nuclei is reduced to σ = 0.032 fm after we have removed some nuclei with special shell effect and isotope chains. These results show that the new relation proposed in this paper can be comparable to $ A^{1/3} $ and $ Z^{1/3} $ formulas with corrections.Moreover, we study the 884 and 791 nuclear mass densities by using L-M neural network method to build description and prediction models. Comparing with CR2013, the RMSDs of nuclear charge radius are σ = 0.018 fm and σ = 0.014 fm, respectively. The RMSDs are reduced by about 50% compared with that from the empirical formula with corrections, and the predicted values are closer to the experimental values measured in recent years.
      通信作者: 焦宝宝, baobaojiao91@126.com
    • 基金项目: 核技术应用教育部工程研究中心开放基金(批准号: HJSJYB2022-9)、江西省教育厅科学技术研究项目(批准号: GJJ2200782)、东华理工大学实验技术开发项目(批准号: DHSY-202251)和东华理工大学博士科研启动基金(批准号: DHBK2019151)资助的课题
      Corresponding author: Jiao Bao-Bao, baobaojiao91@126.com
    • Funds: Project supported by the Open Funds of Engineering Research Center of Nuclear Technology Application, Ministry of Education, China (Grant No. HJSJYB2022-9), the Science and Technology Research Program of the Education Department of Jiangxi Province, China (Grant No. GJJ2200782), the Experimental Technology Development Program of East China University of Technology (Grant No. DHSY-202251), and the Doctoral Scientific Research Foundation of East China University of Technology (Grant No. DHBK2019151)
    [1]

    Campbell P, Moore I D, Pearson M R 2016 Prog. Part. Nucl. Phys. 86 127Google Scholar

    [2]

    Cheal B, Flanagan K T 2010 J. Phys. G: Nucl. Par. 37 113101Google Scholar

    [3]

    Blaum K, Dilling J, Nötershäser M 2013 Phys. Scr. T152 014017Google Scholar

    [4]

    Angeli I, Marinova K P 2013 At. Data Nucl. Data Tables 99 69Google Scholar

    [5]

    Marinova K P, Angeli I https://www-nds.iaea.org/radii/.

    [6]

    Angeli I 2004 At. Data Nucl. Data Tables 87 185Google Scholar

    [7]

    Angeli I 1999 International Nuclear Data Commitee online: http://iaeand.iaea.or.at/indcsel.html.

    [8]

    De Groote R P, Billowes J, Binnersley C L, et al. 2020 Nat. Phys. 16 620Google Scholar

    [9]

    Koszorús Á, Yang X F, Jiang W G, et al. 2021 Nat. Phys. 17 439Google Scholar

    [10]

    Bissell M L, Carette T, Flanagan K T, et al. 2016 Phys. Rev. C 93 064318Google Scholar

    [11]

    Xie L, Yang X F, Wraith C, et al. 2019 Phys. Lett. B 797 134805Google Scholar

    [12]

    Nerlo-Pomorska B, Pomorski K 1993 Z. Phys. A 344 359Google Scholar

    [13]

    Nerlo-Pomorska B, Pomorski K 1994 Z. Phys. A 348 169Google Scholar

    [14]

    圣宗强, 樊广伟, 钱建发 2015 物理学报 64 112101Google Scholar

    Sheng Z Q, Fan G W, Qian J F 2015 Acta Phys. Sin. 64 112101Google Scholar

    [15]

    曹颖逾, 郭建友 2020 物理学报 69 162101Google Scholar

    Cao Y Y, Guo J Y 2020 Acta Phys. Sin. 69 162101Google Scholar

    [16]

    Goriely S, Chamel N, Pearson J M 2016 Phys. Rev. C 93 034337Google Scholar

    [17]

    Zhao P W, Li Z P, Yao J M, Meng J 2010 Phys. Rev. C 82 054319Google Scholar

    [18]

    Xia X W, Lim Y, Zhao P W, Liang H Z, Qu X Y, Chen Y, Liu H, Zhang L F, Zhang S Q, Kim Y, Meng J 2018 At. Data Nucl. Data Tables 121-122 1Google Scholar

    [19]

    Iimura H, Buchinger F 2008 Phys. Rev. C 78 067301Google Scholar

    [20]

    Buchinger F, Pearson J M 2005 Phys. Rev. C 72 057305Google Scholar

    [21]

    Dieperink A E L, Van Isacker P 2009 Eur. Phys. J. A 42 269Google Scholar

    [22]

    Wang N, Li T 2013 Phys. Rev. C 88 011301RGoogle Scholar

    [23]

    Stoitsov M V, Dobaczewski J, Nazarewicz W, et al. 2003 Phys. Rev. C 68 054312Google Scholar

    [24]

    Goriely S, Chamel N, Pearson J M 2010 Phys. Rev. C 82 035804Google Scholar

    [25]

    Bao M, Zong Y Y, Zhao Y M, Arima A 2020 Phys. Rev. C 102 014306Google Scholar

    [26]

    Garvey G T, Gerace W J, Jaffe R L, Talmi I, Kelson I 1969 Rev. Mod. Phys. 41 S1Google Scholar

    [27]

    Sun B H, Lu Y, Peng J P, Liu C Y, Zhao Y M 2014 Phys. Rev. C 90 054318Google Scholar

    [28]

    Bao M, Lu Y, Zhao Y M, Arima A 2016 Phys. Rev. C 94 064315Google Scholar

    [29]

    焦宝宝 2022 物理学报 71 152101Google Scholar

    Jiao B B 2022 Acta Phys. Sin. 71 152101Google Scholar

    [30]

    Ma C, Zong Y Y, Zhao Y M, Arima A 2021 Phys. Rev. C 104 014303Google Scholar

    [31]

    Von Weizsäcker C F 1935 Z. Phys 96 431Google Scholar

    [32]

    Möller P, Myers W D, Sagawa H, Yoshida S 2012 Phys. Rev. Lett. 108 052501Google Scholar

    [33]

    Geng L, Toki H, Meng J 2005 Prog. Theor. Phys. 113 785Google Scholar

    [34]

    Goriely S, Chamel N, Pearson J M 2009 Phys. Rev. Lett. 102 152503Google Scholar

    [35]

    Duflo J, Zuker A P 1995 Phys. Rev. C 52 R23Google Scholar

    [36]

    Qi C 2015 J. Phys. G: Nucl. Par. 42 045104Google Scholar

    [37]

    Wang N, Liang Z, Liu M, et al. 2010 Phys. Rev. C 82 044304Google Scholar

    [38]

    Garvey G T, Kelson I 1966 Phys. Rev. Lett. 16 197Google Scholar

    [39]

    Fu G J, Lei Y, Jiang H, Zhao Y M, et al. 2011 Phys. Rev. C 84 034311Google Scholar

    [40]

    Jiang H, Fu G J, Zhao Y M, et al. 2010 Phys. Rev. C 82 054317Google Scholar

    [41]

    Jiao B B 2018 Mod. Phys. Lett. A 33 1850156Google Scholar

    [42]

    Jiao B B 2020 Int. J. Mod. Phys. E 29 2050024Google Scholar

    [43]

    Yang G L, Qi B, Wang X D, Qi C 2022 Phys. Rev. C 106 024325Google Scholar

    [44]

    Utama R, Piekarewicz J 2017 Phys. Rev. C 96 044308Google Scholar

    [45]

    Utama R, Piekarewicz J 2018 Phys. Rev. C 97 014306Google Scholar

    [46]

    Zhang H F, Wang L H, Yin J P, Chen P H, Zhang H F 2017 J. Phys. G: Nucl. Par. 44 045110Google Scholar

    [47]

    Niu Z M, Liang H Z 2022 Phys. Rev. C 106 L021303Google Scholar

    [48]

    Shang T S, Li J, Niu Z M 2022 Nucl. Sci. Tech. 33 153Google Scholar

    [49]

    Z M Niu and H Z Liang 2018 Phys. Lett. B 778 48Google Scholar

    [50]

    Ma Y F, Su C, Liu J, Ren Z Z, Xu C, Gao Y H 2020 Phys. Rev. C 101 014304Google Scholar

    [51]

    Audi G, Kondev F G, Wang M, Huang W J, Naimi S 2017 Chin. Phys. C 41 030001Google Scholar

    [52]

    Wang M, Huang W J, Kondev F G, Audi G, Naimi S 2021 Chin. Phys. C 45 030003Google Scholar

    [53]

    卢希庭, 江栋兴, 叶沿林 2000 原子核物理 (北京: 原子能出版社) 第7—9 页

    Lu X T, Jiang D X, Ye Y L 2000 Nuclear Physics (Beijing: Atomic Energy Press) pp7–9 (in Chinese)

    [54]

    Fu G J, Bao M, He Z, Jiang H, Zhao Y M, Arima A 2012 Phys. Rev. C 86 054303

  • 图 1  884个原子核密度$ \rho_\text{m} $

    Fig. 1.  The $ \rho_\text{m} $ of 884 nuclei.

    图 2  884个原子核密度 (绿色折线表示的是原子核密度平均值线, 黑色光滑曲线是基于(6)式得到的拟合线, 粉色竖线分别表示中子数 N = 20, 28, 50, 82 和126的位置)

    Fig. 2.  The green zigzag line is plotted by using the average values of $ \rho_\text{m} $ for nuclei with the same mass number N. The black curve is plotted in terms of empirical formula Eq. (6). The vertical lines are plotted at the major neutron closure N = 20, 28, 50, 82 126.

    图 3  从上到下依次为884个核电荷半径的实验值与(2)式、(6)式、(7)式理论计算值的差值. 粉色竖直虚线分别表示中子数 N = 20, 28, 50, 82 和126的位置

    Fig. 3.  The difference between the experimental value of 884 nuclear charge radii and the theoretical value calculated by Eq.(2),Eq.(6) and Eq.(7), respectively. A dash vertical line is plotted at the major neutron closure N = 20, 28, 50, 82 and 126.

    图 4  核电荷半径的计算值与CR2013数据库中实验值之间的误差. 粉色竖直虚线分别表示中子数 N = 20, 28, 50, 82 和126的位置, 黑色横虚线分别表示质子数 Z = 20, 28, 50 和 82 的位置 (单位: fm)

    Fig. 4.  The difference between the calculated values of nuclear charge radius and experimental values in the CR2013 database. A dash vertical line is plotted at the major neutron closure N = 20, 28, 50, 82 and 126. The dash horizontal line is plotted at the major proton closure Z = 20, 28, 50 and 82 (in units of fm).

    图 5  (a) 和 (b)分别表示 Eu, Gd, Tb, Dy 同位素链核电荷半径与核质量的实验值; (c) 和 (d)分别表示Tm, Yb, Lu, Hf同位素链核电荷半径与核质量的实验值

    Fig. 5.  (a) and (b) represent the nuclear charge radii and nuclear mass of Eu, Gd, Tb, Dy elements, respectively; (c) and (d) represent the nuclear charge radii and nuclear mass of Tm, Yb, Lu, Hf elements, respectively.

    图 6  基于神经网络模型I和II得到的核电荷半径计算值与实验值之间的差值 (单位: fm)

    Fig. 6.  The difference between the calculated values of nuclear charge radius (obtained by neural network model I and II) and experimental values in CR2013 database (in units of fm).

    图 7  利用不同模型结合CR2013数据库预得到的预言值与近几年测得的实验值$ ^{37}\text{K} $[9], $ ^{48}\text{K} $[9], $ ^{64}\text{Cu} $[8,10], $ ^{65}\text{Zn} $[11], $ ^{69}\text{Zn} $[11]之间的差值

    Fig. 7.  The difference between the predicted values of nuclear charge radius (obtained by the CR2013 database) and experimental values $ ^{37}\text{K} $[9], $ ^{48}\text{K} $[9], $ ^{64}\text{Cu} $[8,10], $ ^{65}\text{Zn} $[11], $ ^{69}\text{Zn} $[11] in recent years.

    表 1  不同壳层范围的拟合参数 (单位: u/fm3)

    Table 1.  The parameter of different shell regions (in units of u/fm3).

    Region $C_n$ $\delta1$ $a_n$ $\delta2$
    8 ≤ N ≤ 20 0.276 0.010 –0.828 0.153
    21 ≤ N ≤ 28 0.363 0.007 –2.86 0.171
    29 ≤ N ≤ 39 0.206 0.007 1.649 0.256
    40 ≤ N ≤ 50 0.383 0.005 –5.387 0.217
    51 ≤ N ≤ 66 0.242 0.005 1.812 0.316
    67 ≤ N ≤ 82 0.345 0.007 –4.754 0.557
    83 ≤ N ≤ 104 0.227 0.006 4.774 0.573
    105 ≤ N ≤ 126 0.371 0.003 –9.614 0.433
    127 ≤ N 0.230 0.003 8.088 0.516
    下载: 导出CSV
  • [1]

    Campbell P, Moore I D, Pearson M R 2016 Prog. Part. Nucl. Phys. 86 127Google Scholar

    [2]

    Cheal B, Flanagan K T 2010 J. Phys. G: Nucl. Par. 37 113101Google Scholar

    [3]

    Blaum K, Dilling J, Nötershäser M 2013 Phys. Scr. T152 014017Google Scholar

    [4]

    Angeli I, Marinova K P 2013 At. Data Nucl. Data Tables 99 69Google Scholar

    [5]

    Marinova K P, Angeli I https://www-nds.iaea.org/radii/.

    [6]

    Angeli I 2004 At. Data Nucl. Data Tables 87 185Google Scholar

    [7]

    Angeli I 1999 International Nuclear Data Commitee online: http://iaeand.iaea.or.at/indcsel.html.

    [8]

    De Groote R P, Billowes J, Binnersley C L, et al. 2020 Nat. Phys. 16 620Google Scholar

    [9]

    Koszorús Á, Yang X F, Jiang W G, et al. 2021 Nat. Phys. 17 439Google Scholar

    [10]

    Bissell M L, Carette T, Flanagan K T, et al. 2016 Phys. Rev. C 93 064318Google Scholar

    [11]

    Xie L, Yang X F, Wraith C, et al. 2019 Phys. Lett. B 797 134805Google Scholar

    [12]

    Nerlo-Pomorska B, Pomorski K 1993 Z. Phys. A 344 359Google Scholar

    [13]

    Nerlo-Pomorska B, Pomorski K 1994 Z. Phys. A 348 169Google Scholar

    [14]

    圣宗强, 樊广伟, 钱建发 2015 物理学报 64 112101Google Scholar

    Sheng Z Q, Fan G W, Qian J F 2015 Acta Phys. Sin. 64 112101Google Scholar

    [15]

    曹颖逾, 郭建友 2020 物理学报 69 162101Google Scholar

    Cao Y Y, Guo J Y 2020 Acta Phys. Sin. 69 162101Google Scholar

    [16]

    Goriely S, Chamel N, Pearson J M 2016 Phys. Rev. C 93 034337Google Scholar

    [17]

    Zhao P W, Li Z P, Yao J M, Meng J 2010 Phys. Rev. C 82 054319Google Scholar

    [18]

    Xia X W, Lim Y, Zhao P W, Liang H Z, Qu X Y, Chen Y, Liu H, Zhang L F, Zhang S Q, Kim Y, Meng J 2018 At. Data Nucl. Data Tables 121-122 1Google Scholar

    [19]

    Iimura H, Buchinger F 2008 Phys. Rev. C 78 067301Google Scholar

    [20]

    Buchinger F, Pearson J M 2005 Phys. Rev. C 72 057305Google Scholar

    [21]

    Dieperink A E L, Van Isacker P 2009 Eur. Phys. J. A 42 269Google Scholar

    [22]

    Wang N, Li T 2013 Phys. Rev. C 88 011301RGoogle Scholar

    [23]

    Stoitsov M V, Dobaczewski J, Nazarewicz W, et al. 2003 Phys. Rev. C 68 054312Google Scholar

    [24]

    Goriely S, Chamel N, Pearson J M 2010 Phys. Rev. C 82 035804Google Scholar

    [25]

    Bao M, Zong Y Y, Zhao Y M, Arima A 2020 Phys. Rev. C 102 014306Google Scholar

    [26]

    Garvey G T, Gerace W J, Jaffe R L, Talmi I, Kelson I 1969 Rev. Mod. Phys. 41 S1Google Scholar

    [27]

    Sun B H, Lu Y, Peng J P, Liu C Y, Zhao Y M 2014 Phys. Rev. C 90 054318Google Scholar

    [28]

    Bao M, Lu Y, Zhao Y M, Arima A 2016 Phys. Rev. C 94 064315Google Scholar

    [29]

    焦宝宝 2022 物理学报 71 152101Google Scholar

    Jiao B B 2022 Acta Phys. Sin. 71 152101Google Scholar

    [30]

    Ma C, Zong Y Y, Zhao Y M, Arima A 2021 Phys. Rev. C 104 014303Google Scholar

    [31]

    Von Weizsäcker C F 1935 Z. Phys 96 431Google Scholar

    [32]

    Möller P, Myers W D, Sagawa H, Yoshida S 2012 Phys. Rev. Lett. 108 052501Google Scholar

    [33]

    Geng L, Toki H, Meng J 2005 Prog. Theor. Phys. 113 785Google Scholar

    [34]

    Goriely S, Chamel N, Pearson J M 2009 Phys. Rev. Lett. 102 152503Google Scholar

    [35]

    Duflo J, Zuker A P 1995 Phys. Rev. C 52 R23Google Scholar

    [36]

    Qi C 2015 J. Phys. G: Nucl. Par. 42 045104Google Scholar

    [37]

    Wang N, Liang Z, Liu M, et al. 2010 Phys. Rev. C 82 044304Google Scholar

    [38]

    Garvey G T, Kelson I 1966 Phys. Rev. Lett. 16 197Google Scholar

    [39]

    Fu G J, Lei Y, Jiang H, Zhao Y M, et al. 2011 Phys. Rev. C 84 034311Google Scholar

    [40]

    Jiang H, Fu G J, Zhao Y M, et al. 2010 Phys. Rev. C 82 054317Google Scholar

    [41]

    Jiao B B 2018 Mod. Phys. Lett. A 33 1850156Google Scholar

    [42]

    Jiao B B 2020 Int. J. Mod. Phys. E 29 2050024Google Scholar

    [43]

    Yang G L, Qi B, Wang X D, Qi C 2022 Phys. Rev. C 106 024325Google Scholar

    [44]

    Utama R, Piekarewicz J 2017 Phys. Rev. C 96 044308Google Scholar

    [45]

    Utama R, Piekarewicz J 2018 Phys. Rev. C 97 014306Google Scholar

    [46]

    Zhang H F, Wang L H, Yin J P, Chen P H, Zhang H F 2017 J. Phys. G: Nucl. Par. 44 045110Google Scholar

    [47]

    Niu Z M, Liang H Z 2022 Phys. Rev. C 106 L021303Google Scholar

    [48]

    Shang T S, Li J, Niu Z M 2022 Nucl. Sci. Tech. 33 153Google Scholar

    [49]

    Z M Niu and H Z Liang 2018 Phys. Lett. B 778 48Google Scholar

    [50]

    Ma Y F, Su C, Liu J, Ren Z Z, Xu C, Gao Y H 2020 Phys. Rev. C 101 014304Google Scholar

    [51]

    Audi G, Kondev F G, Wang M, Huang W J, Naimi S 2017 Chin. Phys. C 41 030001Google Scholar

    [52]

    Wang M, Huang W J, Kondev F G, Audi G, Naimi S 2021 Chin. Phys. C 45 030003Google Scholar

    [53]

    卢希庭, 江栋兴, 叶沿林 2000 原子核物理 (北京: 原子能出版社) 第7—9 页

    Lu X T, Jiang D X, Ye Y L 2000 Nuclear Physics (Beijing: Atomic Energy Press) pp7–9 (in Chinese)

    [54]

    Fu G J, Bao M, He Z, Jiang H, Zhao Y M, Arima A 2012 Phys. Rev. C 86 054303

  • [1] 黄宇航, 陈理想. 基于未训练神经网络的分数傅里叶变换成像. 物理学报, 2024, 73(9): 094201. doi: 10.7498/aps.73.20240050
    [2] 马锐垚, 王鑫, 李树, 勇珩, 上官丹骅. 基于神经网络的粒子输运问题高效计算方法. 物理学报, 2024, 73(7): 072802. doi: 10.7498/aps.73.20231661
    [3] 杨莹, 曹怀信. 量子混合态的两种神经网络表示. 物理学报, 2023, 72(11): 110301. doi: 10.7498/aps.72.20221905
    [4] 方波浪, 王建国, 冯国斌. 基于物理信息神经网络的光斑质心计算. 物理学报, 2022, 71(20): 200601. doi: 10.7498/aps.71.20220670
    [5] 焦宝宝. 基于同位素链核电荷半径的新关系. 物理学报, 2022, 71(15): 152101. doi: 10.7498/aps.71.20212343
    [6] 李靖, 孙昊. 识别Z玻色子喷注的卷积神经网络方法. 物理学报, 2021, 70(6): 061301. doi: 10.7498/aps.70.20201557
    [7] 曹颖逾, 郭建友. 原子核电荷半径的研究. 物理学报, 2020, 69(16): 162101. doi: 10.7498/aps.69.20191643
    [8] 孙立望, 李洪, 汪鹏君, 高和蓓, 罗孟波. 利用神经网络识别高分子链在表面的吸附相变. 物理学报, 2019, 68(20): 200701. doi: 10.7498/aps.68.20190643
    [9] 魏德志, 陈福集, 郑小雪. 基于混沌理论和改进径向基函数神经网络的网络舆情预测方法. 物理学报, 2015, 64(11): 110503. doi: 10.7498/aps.64.110503
    [10] 圣宗强, 樊广伟, 钱建发. 关于原子核电荷半径的研究. 物理学报, 2015, 64(11): 112101. doi: 10.7498/aps.64.112101
    [11] 李欢, 王友国. 一类非线性神经网络中噪声改善信息传输. 物理学报, 2014, 63(12): 120506. doi: 10.7498/aps.63.120506
    [12] 陈铁明, 蒋融融. 混沌映射和神经网络互扰的新型复合流密码. 物理学报, 2013, 62(4): 040301. doi: 10.7498/aps.62.040301
    [13] 李华青, 廖晓峰, 黄宏宇. 基于神经网络和滑模控制的不确定混沌系统同步. 物理学报, 2011, 60(2): 020512. doi: 10.7498/aps.60.020512
    [14] 赵海全, 张家树. 混沌通信系统中非线性信道的自适应组合神经网络均衡. 物理学报, 2008, 57(7): 3996-4006. doi: 10.7498/aps.57.3996
    [15] 王永生, 孙 瑾, 王昌金, 范洪达. 变参数混沌时间序列的神经网络预测研究. 物理学报, 2008, 57(10): 6120-6131. doi: 10.7498/aps.57.6120
    [16] 行鸿彦, 徐 伟. 混沌背景中微弱信号检测的神经网络方法. 物理学报, 2007, 56(7): 3771-3776. doi: 10.7498/aps.56.3771
    [17] 王瑞敏, 赵 鸿. 神经元传输函数对人工神经网络动力学特性的影响. 物理学报, 2007, 56(2): 730-739. doi: 10.7498/aps.56.730
    [18] 王耀南, 谭 文. 混沌系统的遗传神经网络控制. 物理学报, 2003, 52(11): 2723-2728. doi: 10.7498/aps.52.2723
    [19] 谭文, 王耀南, 刘祖润, 周少武. 非线性系统混沌运动的神经网络控制. 物理学报, 2002, 51(11): 2463-2466. doi: 10.7498/aps.51.2463
    [20] 神经网络的自适应删剪学习算法及其应用. 物理学报, 2001, 50(4): 674-681. doi: 10.7498/aps.50.674
计量
  • 文章访问数:  4242
  • PDF下载量:  85
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-02-01
  • 修回日期:  2023-02-19
  • 上网日期:  2023-03-28
  • 刊出日期:  2023-06-05

/

返回文章
返回