参考文献

［1］ Mitchell T M.Machine Learning［M］.NewYork:McGraw-Hill，1997.

［2］ Han M,Liu B.Ensemble of extreme learning machine for remote sensing image classification［J］.Neurocomputing,2015,149:65-70.

［3］ Cambria E,White B.Jumping NLP Curves:A review of natural language processing research［J］.IEEE Computational Intelligence Magazine,2014,9(2):48-57.

［4］ 周志华,王珏.机器学习及其应用［M］.北京:清华大学出版社,2007.

［5］ 唐晓亮.基于神经网络的半监督学习方法研究［D］.大连理工大学,2009.

［6］ King R D,Feng C,Sutherland A.Sutherland C.Statlog:comparison of classification algorithms on large real-world problems［J］.Applied Artificial Intelligence,1995,9(3):289-333.

［7］ Galar M,Fernández A,Barrenechea E,et al.A review on ensembles for the class imbalance problem:bagging-,boosting-,and hybrid-based approaches［J］.IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2012,42(4):463-484.

［8］ Schadt E E,Linderman M D,Sorenson J,et al.Computational solutions to large-scale data management and analysis［J］.Nature Reviews Genetics,2010,11(9):647-657.

［9］ Cover T,Hart P.Nearest neighbor pattern classification［J］.IEEE Transactions on Information Theory,1967,13(1):21-27.

［10］ Goldberg D E,Holland J H.Genetic algorithms and machine learning［J］.Machine learning,1988,3(2):95-99.

［11］ 席裕庚,柴天佑.遗传算法综述［J］.控制理论与应用,1996,13(6):697-708.

［12］ Tso B C K,Mather P M.Classification of multisource remote sensing imagery using a genetic algorithm and markov random fields［J］.IEEE Transactions on Geoscience and Remote Sensing,1999,37(3):1255-1260.

［13］ Quinlan J R.Induction of decision trees［J］.Machine learning,1986,1(1):81-106.

［14］ Valentini G,Masulli F.Ensembles of learning machines［M］.Neural Nets.Springer Berlin Heidelberg,2002.

［15］ Bühlmann P.Bagging,boosting and ensemble methods［M］.Springer Berlin Heidelberg,2012.

［16］ Bailly J S,Arnaud M,Puech C.Boosting:A classification method for remote sensing［J］.International Journal of Remote Sensing,2007,28(7):1687-1710.

［17］ Budka M,Gabrys B.Density preserving sampling:robust and efficient alternative to cross-validation for error estimation［J］.IEEE Transactions on Neural Networks and Learning Systems,2013,24(1):22-34.

［18］ Dietterich T,Bakiri G.Solving multiclass learning problems via error-correcting out put codes［J］.Journal of Artificial Intelligence Research,1995,2:263-286.

［19］ Ratner B.Statistical and machine-learning data mining:techniques for better predictive modeling and analysis of big data［M］.CRC Press,2011.

［20］ Kononenko I.Estimating attributes:analysis and extension of RELIEF［C］.Proceedings of the 1994 European Conference on Machine Learning.Amsterdan,Springer,1994:171-182.

［21］ Lerman K,Minton S,Knoblock C A.Wrapper maintenance:A machine learning approach［J］.Journal of Artificial Intelligence Research,2003,18:149-181.

［22］ Hong X,Chen S,Qatawneh A,et al.A radial basis function network classifier to maximise leave-one-out mutual information［J］.Applied Soft Computing,2014,23:9-18..

［23］ Lowe D G.Similarity metric learning for a variable-kernel classifier［J］.Neural Computation 1995,7(1):72-85.

［24］ Shahshahani B M,Landgrebe D A.The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon［J］.IEEE Transactions on Geoscience and Remote sensing,1994,32(5):1087-1095.

［25］ Miller D J,Uyar H S.A mixture of experts classifier with learning based on both labelled and unlabelled data［C］.Proceedings of the 9th Processing Systems on Advances in Neural Information,Cambridge,MA:MIT Press,1997:571-577.

［26］ Patra S,Bruzzone L.A fast cluster-assumption based active-learning technique for classification of remote sensing images［J］.IEEE Transactions on Geoscience and Remote sensing,2011,49(5):1617-1626.

［27］ Maulik U,Chakraborty D.Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery［J］.ISPRS Journal of Photogrammetry and Remote sensing,2013,77:66-78.

［28］ Joachims T.Transductive inference for text classification using support vector machines［C］.Proceedings of the 16th International Conference on Machine Learning.Morgan Kaufmann Publishers Inc,1999:200-209.

［29］ Lawrence N D,Jordan M I.Semi-supervised learning via Gaussian processes［C］.Proceedings of 17th Advances Conference on Neural Information Processing Systems,Cambridge,MA:MIT Press,2005:753-760.

［30］ Grandvalet Y,Bengio Y.Semi-supervised learning by entropy minimization［C］.Proceedings of 17th Advances Conference on Neural Information Processing Systems,Cambridge,MA:MIT Press,2005:529-536.

［31］ Tuia D,Volpi M,Trolliet M,et al.Semisupervised manifold alignment of multimodal remote sensing images［J］.IEEE Transactions on Geoscience and Remote sensing,2014,52(12):7708-7720.

［32］ Zhu X,Ghahramani Z,Lafferty J.Semi-supervised learning using Gaussian fields and harmonic functions［C］.Proceedings of the 20th International Conference on Machine Learning,Washington,DC,2003:912-919.

［33］ Zhou D,Bousquet O,Lal T N,et al.Learning with local and global consistency［C］.Proceedings of 16th Advances Conference on Neural Information Processing Systems,Cambridge,MA:MIT Press,2004:321-328.

［34］ Reitmaier T,Calma A,Sick B.Transductive active learning a new semi-supervised learning approach based on iteratively refined generative models to capture structure in data ［J］.Information Sciences,2015,293:275-298.

［35］ McLachlan G J,Krishnan T.The EM algorithm and extensions ［M］.New Jersey:John Wiley and Sons,2007.

［36］ 周鑫.基于EM算法的G0分布参数最大似然估计［J］.电子学报,2013,41(1):178-184.

［37］ Tang X,Han M.Semi-supervised Bayesian artmap［J］.Applied Intelligence,2010,33(3):302-317.

［38］ Mumtaz A,Coviello E,Lanckriet G R,et al.Clustering dynamic textures with the hierarchical EM algorithm for modeling video［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,35(7):1606-1621.

［39］ Moser G,Zerubia J,Serpico S B.Dictionary-based stochastic expectation-maximization for SAR amplitude probability density function estimation［J］.IEEE Transactions on Geoscience and Remote sensing,2006,44(1):188-200.

［40］ Saint-Jean C,Frelicot C.A robust semi-supervised EM-based clustering algorithm with a reject option［C］.Proceedings of International Conference on Pattern Recognition,2002,16(3):399-402.

［41］ Amini M R,Gallinari P.Semi-supervised learning with an imperfect supervisor［J］.Knowledge and Information Systems,2005,8(4):385-413.

［42］ Come E,Oukhellou L,Denoeux T,et al.Learning from partially supervised data using mixture models and belief functions［J］.Pattern Recognition,2009,42(3):334-348.

［43］ Constantinopoulos C,Likas A.Semi-supervised and active learning with the probabilistic RBF classifier［J］.Neurocomputing,2008,71(13):2489-2498.

［44］ Zhang Y,Wen J,Wang X,et al.Semi-supervised learning combining co-training with active learning［J］.Expert Systems with Applications,2014,41(5):2372-2378.

［45］ Zhang X,Song Q,Liu R,et al.Modified co-training with spectral and spatial views for semi-supervised hyperspectral image classification［J］.IEEE Journal of Selected Topics in Applied Earth Observations and Remote sensing,2014,7(6):2044-2055.

［46］ Goldman S,Zhou Y.Enhancing supervised learning with unlabeled data［C］.Proceedings of the 17th International Conference on Machine Learning,San Francisco,CA,2000:327-334.

［47］ Li K,Zhang J,Xu H,et al.A semi-supervised extreme learning machine method based on co-training［J］.Journal of Computational Information Systems,2013,9(1):207-214.

［48］ Zhou Z H,Li M.Tri-training:Exploiting unlabeled data using three classifiers［J］.IEEE Transactions on Knowledge and Data Engineering,2005,17(11):1529-1541.

［49］ Tang X L,Han M.Ternary reversible extreme learning machines:the incremental tri-training method for semi-supervised classification［J］.Knowledge and information systems,2010,23(3):345-372.

［50］ Zhang X,Bai B,Li Y.Tri-training based on neural network ensemble algorithm［M］.Intelligent Science and Intelligent Data Engineering.Springer Berlin Heidelberg,2012:43-49.

［51］ Angluin D,Laird P.Learning from noisy examples［J］.Machine Learning,1988,2(4):343-370.

［52］ Li M.Zhou Z H.Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples［J］.IEEE Transactions on Systems,Man,and Cybernetics Part A:Systems and Humans,2007,37(6):1088-1098.

［53］ Srijith P K,Shevade S,Sundararajan S.Semi-supervised Gaussian process ordinal regression［M］.Machine Learning and Knowledge Discovery in Databases.Springer Berlin Heidelberg,2013:144-159.

［54］ Szummer M,Jaakkola T S.Information regularization with partially labeled data［C］.Proceedings of 14th Advances Conference on Neural Information Processing Systems,2002:1025-1032.

［55］ Grandvalet Y,Bengio Y.Semi-supervised learning by entropy minimization［C］.Proceedings of 17th Advances Conference on Neural Information Processing Systems,2005:529-536.

［56］ Chen Y S,Wang G P.Dong S H.Learning with progressive transductive support vector machine［J］.Pattern Recognition Letters,2003.24(12):1845-1855.

［57］ Bruzzone L,Chi M M,Marconcini M.A novel transductive SVM for semisupervised classification of remote-sensing images［J］.IEEE Transactions on Geoscience and Remote sensing,2006,44(11):3363-3373.

［58］ Zhang R,Wang W J,Ma Y C,et al.Least square transduction support vector machine［J］.Neural Processing Letters,2009,29(2):133-142.

［59］ Li Y Q,Guan C T.Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm［J］.Machine Learning,2008,71(1):33-53.

［60］ Adankon M M,Cheriet M.Learning semi-supervised SVM with genetic algorithm［C］.Proceedings of International Joint Conference on Neural Networks,Orlando,FL,2007:1825-1830.

［61］ Adankon M M,Cheriet M.Help-Training for semi-supervised support vector machines［J］.Pattern Recognition,2011,44(9):2220-2230.

［62］ 李宇峰,黄圣君,周志华.一种基于正则化的半监督多标记学习方法［J］.计算机研究与发展,2012,49(6):1272-1278.

［63］ Maulik U,Chakraborty D.Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery［J］.ISPRS Journal of Photogrammetry and Remote sensing,2013,77:66-78.

［64］ Blum A,Lafferty J,Rwebangira M R,et al.Semi-Supervised Learning Using Randomized Mincuts［C］.Proceedings of International Conference on Machine Learning,2004:97-104.

［65］ Lezoray O,Elmoataz A,Bougleux S.Graph regularization for color image processing［J］.Computer Vision and Image Understanding,2007,107(1):38-55.

［66］ Faigle U,Schonhuth A.Efficient tests for equivalence of hidden Markov processes and quantum random walks［J］.IEEE Transactions on Information Theory,2011,57(3):1746-1753.

［67］ Camps-Valls G T.Marsheva V B,Zhou D Y.Semi-supervised graph-based hyperspectral image classification［J］.IEEE Transactions on Geoscience and Remote sensing,2013,45(10),3044-3054.

［68］ Camps-Valls G,Shervashidze N,Borgwardt K M.Spatio-spectral remote sensing image classification with graph kernels［J］.IEEE Geoscience and Remote sensing Letters,2010,7(4):741-745.

［69］ Kim W,Crawford M M,Ghosh J.Spatially adapted manifold learning for classification of hyperspectral imagery with insufficient labeled data［C］ Proceedings of IEEE International Geoscience and Remote sensing Symposium(IGARSS),2008,1:213-216.

［70］ Urahama K.Semi-supervised classification with spectral subspace projection of data［J］.IEICE Transactions on Information and Systems,2007,90(1):374-377.

［71］ Wang H J,Nie R H,Liu X N,et al.Constraint projections for semi-supervised affinity propagation［J］.Knowledge-Based Systems,2012,36:315-321.

［72］ Gong Y C,Chen C L.Semi-supervised method for gene expression data classification with Gaussian fields and Harmonic functions［C］.Proceedings of 9th International Conference on Pattern Recognition(ICPR),Tampa,FL,2008:1-4.

［73］ Li C H,Kuo B C,Lin C T,et al.A spatial-contextual support vector machine for remotely sensed image classification［J］.IEEE Transactions on Geoscience and Remote sensing,50(3),784-799.

［74］ Kulis B,Basu S,Dhillon I,et al.Semi-supervised graph clustering:a kernel approach［J］.Machine Learning,2009,74(1):1-22.

［75］ Yang W Y,Zhang S W,Liang W.A graph based subspace semi-supervised learning framework for dimensionality reduction［J］.The 10th European Conference on Computer Vision,Marseille,France,2008:664-677.

［76］ Zhong E H,Xie S H,Fan W,et al.Graph-based iterative hybrid feature selection［C］.Proceedings of the 8th IEEE International Conference on Data Mining,Pisa,Italy,2008:1133-1138.

［77］ Rohban M H,Rabiee H R.Supervised neighborhood graph construction for semi-supervised classification［J］.Pattern Recognition,2012,45(4):1363-1372.

［78］ Wang M,Hua X S,Tang J H,et al.Beyond distance measurement:constructing neighborhood similarity for video annotation［J］.IEEE Transactions on Multimedia,2009,11(3):465-476.

［79］ Han M,Zhu X,Yao W.Remote sensing image classification based on neural network ensemble algorithm［J］.Neurocomputing,2012,78(1):133-138.

［80］ Mather P,Koch M.Computer processing of remotely-sensed images:an introduction［M］.John Wiley&Sons,2011.

［81］ 韩敏.水文与水管理中的遥感技术［M］.北京：中国水利水电出版社,2006.

［82］ Gitelson A A,Peng Y,Arkebauer T J,et al.Relationships between gross primary production,green LAI,and canopy chlorophyll content in maize:Implications for remote sensing of primary production［J］.Remote sensing of Environment,2014,144:65-72.

［83］ 罗小波.遥感图像智能分类及其应用［M］.北京：电子工业出版社,2011.

［84］ Mather P,Tso B.Classification methods for remotely sensed data［M］.CRC press,2009.

［85］ Stanley J N,Lamb D W,Irvine S E,et al.Effect of aluminum neutron probe access tubes on the apparent electrical conductivity recorded by an electromagnetic soil survey sensor［J］.IEEE Geoscience and Remote sensing Letters,2014,11(1):333-336.

［86］ Shi Q,Du B,Zhang L.Spatial coherence-based batch-mode active learning for remote sensing image classification［J］.IEEE Transactions on Image Processing,2015,24(7):2037-2050.

［87］ Wan L,Tang K,Li M,et al.Collaborative active and semi-supervised learning for hyperspectral remote sensing image classification［J］.IEEE Transactions on Geoscience and Remote sensing,2015,53(5):2384-2396.

［88］ Agrawal R K,Bawane N G.Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery［J］.Applied Soft Computing,2015,28:217-225.

［89］ 韩敏,程磊,唐晓亮.Fuzzy ARTMAP神经网络在土地覆盖分类中的应用研究［J］.中国图象图形学报,2005,10(4):415-419.

［90］ Wang W,Sun X,Zhang R,et al.Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data［J］.International Journal of Remote sensing,2006,27(3):617-621.

［91］ Poth A,klaus D,et al.Optimization At Multi-Spectral Land Cover Classification With Fuzzy Clustering And The Kohonen Feature Map［J］.International Journal of Remote sensing,2001,22(8):1423-1439.

［92］ Bakos K L,Gamba P.Hierarchical Hybrid Decision Tree Fusion of Multiple Hyperspectral Data Processing Chains［J］.IEEE Transactions on Geoscience and Remote sensing,2011,49(1):388-394.

［93］ Broomhead D S,Lowe D.Multivariable functional interpolation and adaptive networks［J］.Complex systems［J］,1988,2:321-355.

［94］ Cover T M.Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition［J］.IEEE Transactions on Electronic Computers,1965,(3):326-334.

［95］ 张学工.关于统计学习理论与支持向量机［J］.自动化学报,2000,26(1):32-42.

［96］ Boser B E,Guyon I M,Vapnik V N.A training algorithm for optimal margin classifiers［C］.Proceedings of the 5th annual workshop on Computational learning theory.ACM,1992:144-152.

［97］ Cortes C,Vapnik V.Soft margin classifier.Technical Memorandum［T］,AT&T Bell Labs,1993.

［98］ Vapnik V.The nature of statistical learning theory［M］.Springer,2000.

［99］ Li B,Ou S.Intelligent recognition of lung nodule combining rule-based and C-SVM classifiers［J］.International Journal of Computational Intelligence Systems,2011,4(5):960-976.

［100］ Bai J,Xue P,Zhang X,et al.Parameters optimization and application research of v-support vector machine［J］.International Journal of Advancements in Computing Technology,2013,5(3):180-189.

［101］ Zheng S,Shi W,Liu J,et al.Remote sensing image fusion using multiscale mapped LS-SVM［J］.IEEE Transactions on Geoscience and Remote sensing,2008,46(5):1313-1322.

［102］ Muñoz-Marí J,Bovolo F,Gómez-Chova L,et al.Semisupervised one-class support vector machines for classification of remote sensing data［J］.IEEE Transactions on Geoscience and Remote sensing,2010,48(8):3188-3197.

［103］ Yuan J,Chen Y,Yang X.Advances in Intelligent&Soft Computing［M］.Springer Berlin Heidelberg,2011.

［104］ Zhong X,Li J,Dou H,et al.Fuzzy nonlinear proximal support vector machine for land extraction based on remote sensing image［J］.PloS one,2013,8(7):1-17.

［105］ Chang C C,Pao H K,Lee Y J.An RSVM based two-teachers–one-student semi-supervised learning algorithm［J］.Neural Networks,2012,25:57-69.

［106］ Shen J D.A new smooth support vector machine based on a rational function［J］.Applied Mechanics and Materials,2013,263:2199-2202.

［107］ Grossberg S.Adaptive resonance theory［J］.Scholarpedia,2013,8(5):1569.

［108］ Parsons O,Carpenter G A.ARTMAP neural networks for information fusion and data mining:map production and target recognition methodologies［J］.Neural Networks,2003,16(7):1075-1089.

［109］ Palaniappan R,Paramesran R,Nishida S,et al.A new brain-computer interface design using fuzzy ARTMAP［J］.IEEE Transactions on Neural Systems and Rehabilitation Engineering,2002,10(3):140-148.

［110］ Rubin M A.Application of fuzzy ARTMAP and ART-EMAP to automatic target recognition using radar range profiles［J］.Neural Networks,1995,8(7):1109-1116.

［111］ Downs J,Harrison R F,Kennedy R L,et al.Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks［J］.Artificial Intelligence in Medicine,1996,8(4):403-428.

［112］ Aggarwal R K,Xuan Q Y,Johns A T,et al.A novel approach to fault diagnosis in multicircuit transmission lines using fuzzy ARTmap neural networks［J］.IEEE Transactions on Neural Networks,1999,10(5):1214-1221.

［113］ Tan S C,Rao M V C,Lim C P.A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm［J］.Soft Computing,2008,12(8):765-775.

［114］ Carpenter G A,Markuzon N.ARTMAP-IC and medical diagnosis:Instance counting and inconsistent cases［J］.Neural Networks,1998,11(2):323-336.

［115］ Tan S C,Rao M V C,Lim C P.An online pruning strategy for supervised ARTMAP-based neural networks［J］.Neural Computing&Applications,2009,18(4):387-395.

［116］ Charalampidis D,Kasparis T,Georgiopoulos M.Classification of noisy signals using fuzzy ARTMAP neural networks［J］.IEEE Transactions on Neural Networks,2001,12(5):1023-1036.

［117］ Koufakou A,Georgiopoulos M,Anagnostopoulos G,et al.Cross-validation in Fuzzy ARTMAP for large databases［J］.Neural Networks,2001,14(9):1279-1291.

［118］ Gomez-Sanchez E,Dimitriadis Y A,Cano-Izquierdo J M,et al.mu-ARTMAP:Use of mutual information for category reduction in Fuzzy ARTMAP［J］.IEEE Transactions on Neural Networks,2002,13(1):58-69.

［119］ Vakil-Baghmisheh M T,Pavešic＇N.A fast simplified fuzzy ARTMAP network［J］.Neural Processing Letter,2003,17(3):273-316.

［120］ Williamson J R.Gaussian ARTMAP:A neural network for past incremental learning of noisy multidimensional maps［J］.Neural Networks,1996,9(5):881-897.

［121］ Muchoney D,Williamson J.A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data［J］.IEEE Transactions on Geoscience and Remote sensing,2001,39(9):1969-1977.

［122］ Vigdor B,Lerner B.The Bayesian ARTMAP［J］.IEEE Transactions on Neural Networks,2007,18(6):1628-1644.

［123］ Wang X,Han M.Improved extreme learning machine for multivariate time series online sequential prediction［J］.Engineering Applications of Artificial Intelligence,2015,40:28-36.

［124］ 韩红桂,乔俊飞,薄迎春.基于信息强度的 RBF 神经网络结构设计研究［J］.自动化学报,2012,38(7):1083-1090.

［125］ Chang N,Han M,Yao W,et al.Change detection of land use and land cover in an urban region with SPOT5 images and partial Lanczos extreme learning machine［J］.Journal of Applied Remote sensing,2010,4(1):2816-2832.

［126］ Samat A,Gamba P,Du P,et al.Active extreme learning machines for quad-polarimetric SAR imagery classification［J］.International Journal of Applied Earth Observation and Geoinformation,2015,35:305-319.

［127］ Li S,Wang P,Goel L.Short-term load forecasting by wavelet transform and evolutionary extreme learning machine［J］.Electric Power Systems Research,2015,122:96-103.

［128］ Wei X K,Li Y H.Linear programming minimum sphere set covering for extreme learning machines［J］.Neurocomputing,2008,71(4):570-575.

［129］ 张弦,王宏力.限定记忆极端学习机及其应用［J］.控制与决策,2012,27(8):1206-1210.

[130] Şahin M，Kaya Y，Uyar M，et al.Application of extreme learning machine for esti-mating solar radiation from satellite data [J].International Journal of Energy Re-search，2014，38（2）：205 212.

［131］ Tang X L,Han M.Partial Lanczos extreme learning machine for single-output regression problems［J］.Neurocomputing,2009,72(13),3066-3076.

［132］ 李凡军,韩红桂,乔俊飞.基于灵敏度分析法的 ELM 剪枝算法［J］.控制与决策,2014,29(6):1003-1008.

［133］ Lim J S,Lee S,Pang H S.Low complexity adaptive forgetting factor for online sequential extreme learning machine(OS-ELM)for application to nonstationary system estimations［J］.Neural Computing and Applications,2013,22(3-4):569-576.

［134］ 丛爽,王怡雯.随机神经网络发展现状综述［J］.控制理论与应用,2005,21(6):975-980.

［135］ Gelenbe E.Introduction to the special issue on G-Networks and the random neural network［J］.Performance Evaluation,2011,68(4):307-308.

［136］ Sheng C,Zhao J,Liu Y,et al.Prediction for noisy nonlinear time series by echo state network based on dual estimation［J］.Neurocomputing,2012,82:186-195.

［137］ Wojcik G M.Electrical parameters influence on the dynamics of the Hodgkin-Huxley liquid state machine［J］.Neurocomputing,2012,79:68-74.

［138］ Zhang B,Miller D J,Wang Y.Nonlinear system modeling with random matrices:echo state networks revisited［J］.IEEE Transactions on Neural Networks and Learning Systems,2012,23(1):175-182.

［139］ 宋彤,李菡.基于小波回声状态网络的混沌时间序列预测［J］.物理学报,2012,61(8):90-96.

［140］ Yildiz L B,Jaeger H,Kiebel S J.Re-visiting the echo state property［J］.Neural Networks,2012,35:1-9.

［141］ Steil J J.Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning［J］.Neural Networks,2007,20(3):353-364.

［142］ Haykin S.Neural networks:a comprehensive foundation［M］.Englewood Cliffs,NJ:Prentice Hall,2007.

［143］ 张学清,梁军.基于EEMD-近似熵和储备池的风电功率混沌时间序列预测模型［J］.物理学报,2013,62(5):1-10.

［144］ 彭宇,王建民,彭喜元.基于回声状态网络的时间序列预测方法研究［J］.电子学报,2010,38(2A):148-154.

［145］ Buehner M,Young P.A tighter bound for the echo state property［J］.IEEE Transactions on Neural Networks,2006,17(3):820-824.

［146］ 韩敏.基于储备池的非线性系统预测理论与分析方法［M］.北京：中国水利水电出版社,2011.