Evolving neural network with extreme learning for system modeling

This p aper introduces an evolving feedforward single hidden layer neural network with extreme learning. The evolving neural network simultaneously adapts its structure and updates its weights using recursive algorithms. Neurons in the hidden layer are added whenever necessary by the implicit nature of the input data. The number of neurons in the hidden layer is found using a recursive granulation algorithm based on the concept of cloud. A cloud is a collection of points whose density implicitly defines a cluster. An extreme learning-based algorithm is used to compute hidden and output layers weights of the neural network. Computational results show that the evolving neural network modeling approach is competitive when compared with alternative evolving modeling approaches.

[1]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[2]  Guang-Bin Huang,et al.  Reply to “Comments on “The Extreme Learning Machine”” , 2008, IEEE Transactions on Neural Networks.

[3]  Plamen P. Angelov,et al.  A new type of simplified fuzzy rule-based system , 2012, Int. J. Gen. Syst..

[4]  Fangju Ai.  A new pruning algorithm for Feedforward Neural Networks , 2011, The Fourth International Workshop on Advanced Computational Intelligence.

[5]  P. Angelov,et al.  Evolving Fuzzy Systems from Data Streams in Real-Time , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[6]  H. Rajabi Mashhadi,et al.  Static security assessment using radial basis function neural networks based on growing and pruning method , 2010, 2010 IEEE Electrical Power & Energy Conference.

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

[8]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[9]  Dejan Dovzan,et al.  Solving the sales prediction problem with fuzzy evolving methods , 2012, 2012 IEEE Congress on Evolutionary Computation.

[10]  Dejan Dovzan,et al.  Recursive clustering based on a Gustafson–Kessel algorithm , 2011, Evol. Syst..

[11]  Amaury Lendasse,et al.  Evolving fuzzy optimally pruned extreme learning machine for regression problems , 2010, Evol. Syst..

[12]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[13]  Plamen P. Angelov,et al.  Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density , 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

[14]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[15]  Xin Yao,et al.  A New Adaptive Merging and Growing Algorithm for Designing Artificial Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Zhu Yu.  A Constructive Neural Network Learning Method Based on Quotient Space and Its Application in Coal Mine Gas Prediction , 2010 .

[17]  Plamen P. Angelov,et al.  Density-based averaging - A new operator for data fusion , 2013, Inf. Sci..

[18]  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).

[19]  Wenjian Wang,et al.  Optimal feed-forward neural networks based on the combination of constructing and pruning by genetic algorithms , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[20]  Wei Gao.  New evolutionary neural networks , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[21]  Fernando Bordignon,et al.  Extreme Learning for Evolving Hybrid Neural Networks , 2012, 2012 Brazilian Symposium on Neural Networks.

[22]  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).

[23]  Ana Carolina Lorena,et al.  Evolutionary neural networks applied to keystroke dynamics: Genetic and immune based , 2012, 2012 IEEE Congress on Evolutionary Computation.

[24]  Amaury Lendasse,et al.  Evolving fuzzy Optimally Pruned Extreme Learning Machine: A comparative analysis , 2010, International Conference on Fuzzy Systems.

[25]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[26]  Léon Personnaz,et al.  Neural-network construction and selection in nonlinear modeling , 2003, IEEE Trans. Neural Networks.

[27]  Fernando A. C. Gomide,et al.  Evolving Hybrid Neural Fuzzy Network for System Modeling and Time Series Forecasting , 2013, 2013 12th International Conference on Machine Learning and Applications.