Hardware implementation methods in Random Vector Functional-Link Networks
暂无分享,去创建一个
[1] Amaury Lendasse,et al. OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.
[2] Z. Dong,et al. Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis , 2006, IEEE Transactions on Power Systems.
[3] Zhan-Li Sun,et al. Application of the LP-ELM Model on Transportation System Lifetime Optimization , 2011, IEEE Transactions on Intelligent Transportation Systems.
[4] Manuel Carcenac,et al. A modular neural network for super-resolution of human faces , 2009, Applied Intelligence.
[5] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[6] De-gan Zhang. A new approach and system for attentive mobile learning based on seamless migration , 2010, Applied Intelligence.
[7] Yoh-Han Pao,et al. Stochastic choice of basis functions in adaptive function approximation and the functional-link net , 1995, IEEE Trans. Neural Networks.
[8] Joseph R. Cavallaro,et al. FPGA Implementation of Matrix Inversion Using QRD-RLS Algorithm , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..
[9] Z.Y. Dong,et al. Earlier detection of risk of blackout by real-time dynamic security assessment based on Extreme Learning Machines , 2010, 2010 International Conference on Power System Technology.
[10] Åke Björck,et al. Numerical methods for least square problems , 1996 .
[11] Yew-Soon Ong,et al. Extreme learning machine for multi-categories classification applications , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[12] George W. Irwin,et al. Improving neural network training solutions using regularisation , 2001, Neurocomputing.
[13] Carl D. Meyer,et al. Matrix Analysis and Applied Linear Algebra , 2000 .
[14] Narasimhan Sundararajan,et al. ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[15] Tammy Noergaard,et al. Embedded systems architecture - a comprehensive guide for engineers and programmers , 2005 .
[16] Ching-Wei Wang,et al. Boosting-SVM: effective learning with reduced data dimension , 2013, Applied Intelligence.
[17] Kit Po Wong,et al. Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping , 2012, IEEE Transactions on Power Systems.
[18] Dejan J. Sobajic,et al. Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.
[19] N. S. Marimuthu,et al. Intelligent approaches using support vector machine and extreme learning machine for transmission line protection , 2010, Neurocomputing.
[20] Q. M. Jonathan Wu,et al. Human action recognition using extreme learning machine based on visual vocabularies , 2010, Neurocomputing.
[21] Han Wang,et al. Ensemble Based Extreme Learning Machine , 2010, IEEE Signal Processing Letters.
[22] George W. Irwin,et al. A hybrid linear/nonlinear training algorithm for feedforward neural networks , 1998, IEEE Trans. Neural Networks.
[23] K. S. Banerjee. Generalized Inverse of Matrices and Its Applications , 1973 .
[24] Jianren Wang,et al. Extreme Learning Machine for Bank Clients Classification , 2009, 2009 International Conference on Information Management, Innovation Management and Industrial Engineering.
[25] Ramón Moreno,et al. A machine learning based intelligent vision system for autonomous object detection and recognition , 2013, Applied Intelligence.
[26] Yves Chauvin,et al. Backpropagation: theory, architectures, and applications , 1995 .
[27] Maurizio Valle,et al. Tactile-Data Classification of Contact Materials Using Computational Intelligence , 2011, IEEE Transactions on Robotics.
[28] Ryan Kastner,et al. Automatic generation of decomposition based matrix inversion architectures , 2008, 2008 International Conference on Field-Programmable Technology.
[29] Zhi-Zhong Mao,et al. An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace , 2010, IEEE Transactions on Automation Science and Engineering.
[30] Hongwei Sun,et al. Accelerating FCM neural network classifier using graphics processing units with CUDA , 2013, Applied Intelligence.
[31] Yi Zhao,et al. A protein secondary structure prediction framework based on the Extreme Learning Machine , 2008, Neurocomputing.
[32] David R. Kincaid,et al. Numerical mathematics and computing , 1980 .
[33] J. Ross Quinlan,et al. Combining Instance-Based and Model-Based Learning , 1993, ICML.
[34] L. P. Wang,et al. Comments on "The Extreme Learning Machine" , 2008, IEEE Trans. Neural Networks.
[35] Paolo Gastaldo,et al. Efficient Digital Implementation of Extreme Learning Machines for Classification , 2012, IEEE Transactions on Circuits and Systems II: Express Briefs.
[36] Santa Barbara,et al. Implementation of QR Decomposition Algorithm using FPGAs , 2007 .
[37] Andrew G. Schmidt,et al. Embedded Systems Design with Platform FPGAs: Principles and Practices , 2010 .
[38] Luis Gómez-Chova,et al. An IP Core and GUI for Implementing Multilayer Perceptron with a Fuzzy Activation Function on Configurable Logic Devices , 2008, J. Univers. Comput. Sci..
[39] James E. Gentle,et al. Matrix Algebra: Theory, Computations, and Applications in Statistics , 2007 .
[40] Å. Björck. Solving linear least squares problems by Gram-Schmidt orthogonalization , 1967 .
[41] Tianyou Chai,et al. A model for steel billet temperature of prediction of heating furnace , 2010, Proceedings of the 29th Chinese Control Conference.
[42] D. Rubinfeld,et al. Hedonic housing prices and the demand for clean air , 1978 .
[43] Ching-Tsorng Tsai,et al. Nonlinear quantization on Hebbian-type associative memories , 2011, Applied Intelligence.
[44] C. R. Rao,et al. Generalized Inverse of Matrices and its Applications , 1972 .
[45] Tamás Szabó,et al. An Efficient Hardware Implementation of Feed-Forward Neural Networks , 2001, IEA/AIE.
[46] Shuzhi Sam Ge,et al. Real-time face detection for human robot interaction , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.
[47] Jaime S. Cardoso,et al. A new linear parametrization for peak friction coefficient estimation in real time , 2010, 2010 IEEE Vehicle Power and Propulsion Conference.
[48] Al Geist,et al. The BR Eigenvalue Algorithm , 1997, SIAM J. Matrix Anal. Appl..
[49] D. Kilpatrick,et al. Numeric Prediction Using Instance-Based Learning with Encoding Length Selection , 1997, ICONIP.
[50] Walter Gander,et al. Algorithms for the QR-Decomposition , 2003 .
[51] Ryan Kastner,et al. An FPGA Design Space Exploration Tool for Matrix Inversion Architectures , 2008, 2008 Symposium on Application Specific Processors.
[52] A.H. Nizar,et al. Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method , 2008, IEEE Transactions on Power Systems.
[53] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[54] Dianhui Wang,et al. Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..
[55] Narasimhan Sundararajan,et al. Classification of abnormalities in digitized mammograms using Extreme Learning Machine , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.
[56] Pietro Lio,et al. Epileptic EEG Detection via a Novel Pattern Recognition Framework , 2011, 2011 5th International Conference on Bioinformatics and Biomedical Engineering.
[57] El-Ghazali Talbi,et al. Metaheuristics - From Design to Implementation , 2009 .
[58] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[59] Amit Agarwal,et al. A new machine learning paradigm for terrain reconstruction , 2006, IEEE Geoscience and Remote Sensing Letters.
[60] Yong Yu,et al. Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..