Back Propagation Convex Extreme Learning Machine

Recently, extreme learning machine has greatly improved in training speed and learning effectiveness of feedforward neural network which includes one hidden layer. However, the random initialization of ELM model parameters can bring randomness and affect generalization ability. The paper proposed back propagation convex extreme learning machine (BP-CELM), in which the hidden layer parameters \( {\mathbf{(a}},\,{\mathbf{b}}) \) can be calculated by formulas. The convergence of BP-CELM is proved in the paper. Simulation results show that BP-CELM has higher training speed and better generalization performance than other randomized neural network algorithms.

[1]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[2]  Yaonan Wang,et al.  Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Chang Feng,et al.  Meta-ELM: ELM with ELM hidden nodes , 2014, Neurocomputing.

[4]  Raveendran Paramesran,et al.  Hessian semi-supervised extreme learning machine , 2016, Neurocomputing.

[5]  Xinping Guan,et al.  Identification and control of nonlinear system based on Laguerre-ELM Wiener model , 2016, Commun. Nonlinear Sci. Numer. Simul..

[6]  Yimin Yang,et al.  Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification , 2016, IEEE Transactions on Cybernetics.

[7]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[8]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[9]  Yaonan Wang,et al.  Data Partition Learning With Multiple Extreme Learning Machines , 2015, IEEE Transactions on Cybernetics.

[10]  Zhiping Lin,et al.  Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey , 2015 .

[11]  Cheng Wu,et al.  Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.

[12]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[13]  Jun Miao,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[14]  Yaonan Wang,et al.  Neural network-based self-learning control for power transmission line deicing robot , 2011, Neural Computing and Applications.

[15]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.