Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Future Generation Computer Systems - Special issue on metacomputing
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
A Neural Network Based Predictive Mechanism for Available Bandwidth
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
IEEE Transactions on Parallel and Distributed Systems
Grid Resource Prediction Based on Support Vector Regression and Genetic Algorithms
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 01
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Bandwidth prediction and congestion control for ABR traffic based on neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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In order to manage the grid resources more effectively, the prediction information of grid resources is necessary in the grid system This study developed a new model, ISGA-SVR, for parameters optimization in support vector regression (SVR), which is then applied to grid resources prediction In order to build an effective SVR model, SVR's parameters must be selected carefully Therefore, we develop genetic algorithms with improved simulated binary crossover (ISBX) that can automatically determine the optimal parameters of SVR with higher predictive accuracy In ISBX, we proposed a new method to deal with the bounded search space This method can improve the search ability of original simulated binary crossover (SBX) .The proposed model was tested with grid resources benchmark data set Experimental results demonstrated that ISGA-SVR worked better than SVR optimized by genetic algorithm with SBX(SGA-SVR) and back-propagation neural network (BPNN).