| Citation: | LU Cheng, XU Tingxue, WANG Honget al. A fault diagnosis model of plasticity echo state network based on L1/2-norm regularization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(3): 535-541. doi: 10.13700/j.bh.1001-5965.2017.0214(in Chinese) |
In order to improve the dynamic adaptability of reservoir, overcome the ill-posed problems of output weights in echo state network (ESN), and balance the fitting and generalization ability, a fault diagnosis model of plasticity echo state network based on
| [1] |
CHINE W, MELLIT A, LUGHI V, et al.A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks[J].Renewable Energy, 2016, 90:501-512. doi: 10.1016/j.renene.2016.01.036
|
| [2] |
UNAL M, ONAT M, DEMETGUL M, et al.Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network[J].Measurement, 2014, 58:187-196. doi: 10.1016/j.measurement.2014.08.041
|
| [3] |
SHATNAWI Y, AL-KHASSAWENEH M.Fault diagnosis in internal combustion engines using extension neural network[J].IEEE Transactions on Industrial Electronics, 2014, 61(3):1434-1443. doi: 10.1109/TIE.2013.2261033
|
| [4] |
JAEGER H. The "echo state" approach to analysing and training recurrent neural networks-with an erratum note[R]. Bonn: German National Research Center for Information Technology GMD Technical Report, 2001.
|
| [5] |
LUN S X, YAO X S, QI H Y, et al.A novel model of leaky integrator echo state network for time-series prediction[J].Neurocomputing, 2015, 159:58-66.
|
| [6] |
VARSHNEY S, VERMA T.Half hourly electricity load prediction using echo state network[J].International Journal of Science and Research, 2014, 3(6):885-888.
|
| [7] |
MORANDO S, JEMEI S, HISSEL D, et al.ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network[J].Mathematics and Computers in Simulation, 2017, 131:283-294. doi: 10.1016/j.matcom.2015.06.009
|
| [8] |
许美玲, 韩敏.多元混沌时间序列的因子回声状态网络预测模型[J].自动化学报, 2015, 41(5):1042-1046.
XU M L, HAN M.The model of factor echo state network prediction for multivariate chaotic time series[J].Acta Automatica Sinica, 2015, 41(5):1042-1046(in Chinese).
|
| [9] |
郭嘉, 雷苗, 彭喜元.基于相应簇回声状态网络静态分类方法[J].电子学报, 2011, 39(3A):14-18.
GUO J, LEI M, PENG X Y.Static classification method based on corresponding cluster echo state network[J].Acta Sinica, 2011, 39(3A):14-18(in Chinese).
|
| [10] |
SCARDAPANE S, UNCINI A.Semi-supervised echo state networks for audio classification[J].Cognitive Computation, 2017, 9(1):125-135. doi: 10.1007/s12559-016-9439-z
|
| [11] |
SONG Q S, FENG Z R.Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series[J].Neurocomputing, 2010, 73(10-12):2177-2185. doi: 10.1016/j.neucom.2010.01.015
|
| [12] |
MARTIN C E, REGGIA J A.Fusing swarm intelligence and self-assembly for optimizing echo state networks[J].Computational Intelligence and Neuroscience, 2015, 2015(5-6):642429.
|
| [13] |
DUTOIT X, SCHRAUWEN B, VAN CAMPENHOUT J, et al.Pruning and regularization in reservoir computing[J].Neurocomputing, 2009, 72(7):1534-1546.
|
| [14] |
KUMP P, BAI E W, CHAN K, et al.Variable selection via RIVAL(removing irrelevant variables amidst Lasso iterations) and its application to nuclear material detection[J].Automatica, 2012, 48(9):2107-2115. doi: 10.1016/j.automatica.2012.06.051
|
| [15] |
SHI Z, HAN M.Support vector echo-state machine for chaotic time-series prediction[J].IEEE Transactions on Neural Networks, 2007, 18(2):359-372. doi: 10.1109/TNN.2006.885113
|
| [16] |
刘建伟, 李双成, 罗雄麟.p范数正则化支持向量机分类算法[J].自动化学报, 2012, 38(1):76-87.
LIU J W, LI S C, LUO X L.Classification algorithm of support vector machine via p-norm regularization[J].Acta Automatica Sinica, 2012, 38(1):76-87(in Chinese).
|
| [17] |
韩敏, 李德才.基于替代函数及贝叶斯框架的1范数ELM算法[J].自动化学报, 2011, 37(11):1344-1350.
HAN M, LI D C.An norm 1 regularization term ELM algorithm based on surrogate function and Bayesian framework[J].Acta Automatica Sinica, 2011, 37(11):1344-1350(in Chinese).
|
| [18] |
ZOU H, HASTIE T.Regularization and variable selection via the elastic net[J].Journal of the Royal Statistical Society, 2005, 67(2):301-320. doi: 10.1111/j.1467-9868.2005.00527.x/full
|
| [19] |
LUKOŠEVIČIUS M, JAEGER H.Reservoir computing approaches to recurrent neural network training[J].Computer Science Review, 2009, 3(3):127-149. doi: 10.1016/j.cosrev.2009.03.005
|
| [20] |
CASTELLANI G C, INTRATOR N, SHOUVAL H, et al.Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons[J].Network:Computation in Neural Systems, 1999, 10(2):111-121. doi: 10.1088/0954-898X_10_2_001
|
| [21] |
LEFORT M, BONIFACE Y, GIRAU B. Self-organization of neural maps using a modulated BCM rule within a multimodal architecture[C]//Brain Inspired Cognitive Systems 2010. Berlin: Springer, 2010: 26-38.
|
| [22] |
TIBSHIRANI R.Regression shrinkage and selection via the lasso[J].Journal of the Royal Statistical Society, 1996, 58(1):267-288.
|
| [23] |
彭义刚, 索津莉, 戴琼海, 等.从压缩传感到低秩矩阵恢复:理论与应用[J].自动化学报, 2013, 39(7):981-994.
PENG Y G, SUO J L, DAI Q H, et al.From compressed sensing to low-rank matrix recovery:Theory and applications[J].Acta Automatica Sinica, 2013, 39(7):981-994(in Chinese).
|
| [24] |
ZOU H.The adaptive lasso and its oracle properties[J].Journal of the American Statistical Association, 2006, 101(476):1418-1429. doi: 10.1198/016214506000000735
|
| [25] |
XU Z, ZHANG H, WANG Y, et al.L 1/2 regularization[J].Science China Information Sciences, 2010, 53(6):1159-1169. doi: 10.1007/s11432-010-0090-0
|
| [26] |
DAUBECHIES I, DEVORE R, FORNASIER M, et al.Iteratively reweighted least squares minimization for sparse recovery[J].Communications on Pure and Applied Mathematics, 2010, 63(1):1-38.
|
| [27] |
XU Z, CHANG X, XU F, et al.L1/2 regularization:A thresholding representation theory and a fast solver[J].IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(7):1013-1027. doi: 10.1109/TNNLS.2012.2197412
|
| [28] |
ZENG J, LIN S, WANG Y, et al.L1/2 regularization:Convergence of iterative half thresholding algorithm[J].IEEE Transactions on Signal Processing, 2014, 62(9):2317-2329. doi: 10.1109/TSP.2014.2309076
|