A new hybrid constructive neural network method for impacting and its application on tungsten price prediction
暂无分享,去创建一个
Liu Hongjuan | Zhu Hao | Muzhou Hou | Yunlei Yang | Liu Taohua | Liu Xinge | Yuan Xiugui | Yunlei Yang | Muzhou Hou | Liu Taohua | Liu Hongjuan | Zhu Hao | Yuan Xiugui | Xinge Liu
[1] Gustavo Deco,et al. Two Strategies to Avoid Overfitting in Feedforward Networks , 1997, Neural Networks.
[2] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[3] Skander Soltani,et al. On the use of the wavelet decomposition for time series prediction , 2002, ESANN.
[4] Lingling Fan,et al. Singular Points Detection Based on Zero-Pole Model in Fingerprint Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Simon Haykin,et al. Neural Networks and Learning Machines , 2010 .
[6] Gregg D. Wilensky,et al. Neural Network Studies , 1993 .
[7] Chien-Jen Huang,et al. Using multi-stage data mining technique to build forecast model for Taiwan stocks , 2011, Neural Computing and Applications.
[8] N Mai Duy,et al. APPROXIMATION OF FUNCTION AND ITS DERIVATIVES USING RADIAL BASIS FUNCTION NETWORKS , 2003 .
[9] Xin Yao,et al. A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.
[10] L. Cooper,et al. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .
[11] C. McGreavy,et al. Application of wavelets and neural networks to diagnostic system development , 1999 .
[12] Marco van Akkeren,et al. A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.
[13] Jooyoung Park,et al. Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.
[14] Vince D. Calhoun,et al. A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function , 2008, IEEE Signal Processing Letters.
[15] MuzhouHou,et al. A new hybrid constructive neural network method for impacting and its application on tungsten price prediction , 2017 .
[16] Tao Zhang,et al. Adaptive neural network control of nonlinear systems by state and output feedback , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[17] Lei Xu,et al. Finite Mixture of ARMA-GARCH Model for Stock Price Prediction , 2003 .
[18] Erkki Oja,et al. A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.
[19] J. Stock,et al. A Probability Model of the Coincident Economic Indicators , 1988 .
[20] Moon Ho Lee,et al. A new constructive neural network method for noise processing and its application on stock market prediction , 2014, Appl. Soft Comput..
[21] Kenji Fukumizu,et al. Relation between weight size and degree of over-fitting in neural network regression , 2008, Neural Networks.
[22] F. J. Sainz,et al. Constructive approximate interpolation by neural networks , 2006 .
[23] Rasmus Berg Palm,et al. Prediction as a candidate for learning deep hierarchical models of data , 2012 .
[24] Guangren Duan,et al. The Design of RBF Neural Networks for Solving Overfitting Problem , 2006, 2006 6th World Congress on Intelligent Control and Automation.
[25] Shaocheng Tong,et al. Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Neural Networks.
[26] Guang-Bin Huang,et al. Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[27] Yichuang Sun,et al. Wavelet neural network approach for fault diagnosis of analogue circuits , 2004 .
[28] Igor V. Tetko,et al. Neural network studies, 1. Comparison of overfitting and overtraining , 1995, J. Chem. Inf. Comput. Sci..
[29] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[30] F. Tay,et al. Application of support vector machines in financial time series forecasting , 2001 .
[31] Yi-Fan Wang,et al. Predicting stock price using fuzzy grey prediction system , 2002, Expert Syst. Appl..
[32] Liang-Yu Shyu,et al. Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.
[33] Meng Joo Er,et al. Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.
[34] Allan Pinkus,et al. Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.
[35] Xuli Han,et al. Constructive Approximation to Multivariate Function by Decay RBF Neural Network , 2010, IEEE Transactions on Neural Networks.
[36] Charles K. Chui,et al. An Introduction to Wavelets , 1992 .
[37] Hou Muzhou,et al. A Self-Organizing Mixture Extreme Leaning Machine for Time Series Forecasting * , 2015 .
[38] Xuehong Zhu,et al. Decomposition laws of tungsten prices fluctuation since 1900 and its applications , 2013 .
[39] G. Lewicki,et al. Approximation by Superpositions of a Sigmoidal Function , 2003 .
[40] Xin Yao,et al. A New Constructive Algorithm for Architectural and Functional Adaptation of Artificial Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[41] S. Liong,et al. GENERALIZATION FOR MULTILAYER NEURAL NETWORK BAYESIAN REGULARIZATION OR EARLY STOPPING , 2004 .
[42] Hong Wang,et al. A direct adaptive neural-network control for unknown nonlinear systems and its application , 1998, IEEE Trans. Neural Networks.
[43] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[44] Francesco Palmieri,et al. Optimal filtering algorithms for fast learning in feedforward neural networks , 1992, Neural Networks.
[45] Stephen Grossberg,et al. The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.
[46] J. Kynický,et al. From "strategic" tungsten to "green" neodymium: a century of critical metals at a glance , 2015 .
[47] Les E. Atlas,et al. Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.
[48] Marijana Zekić. Neural Network Applications in Stock Market Predictions-A Methodology Analysis , .
[49] Marios M. Polycarpou,et al. Stable adaptive neural control scheme for nonlinear systems , 1996, IEEE Trans. Autom. Control..
[50] Bernard Delyon,et al. Accuracy analysis for wavelet approximations , 1995, IEEE Trans. Neural Networks.
[51] Kai Wang,et al. An Expanded Training Set Based Validation Method to Avoid Overfitting for Neural Network Classifier , 2008, 2008 Fourth International Conference on Natural Computation.
[52] Derong Liu,et al. A constructive algorithm for feedforward neural networks with incremental training , 2002 .
[53] Guang-Bin Huang,et al. Convex incremental extreme learning machine , 2007, Neurocomputing.
[54] T.B. Trafalis,et al. Kernel principal component analysis and support vector machines for stock price prediction , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[55] J. S. Sahambi,et al. Classification of ECG arrhythmias using multi-resolution analysis and neural networks , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.
[56] J. P. Castagna,et al. Avoiding overfitting caused by noise using a uniform training mode , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[57] Raphaël Féraud,et al. A Fast and Accurate Face Detector Based on Neural Networks , 2001, IEEE Trans. Pattern Anal. Mach. Intell..