Machine Learning - Special issue on learning with probabilistic representations
Reliable classification using neural networks: a genetic algorithm and backpropagation comparison
Decision Support Systems
Alternative Neural Network Training Methods
IEEE Expert: Intelligent Systems and Their Applications
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
An Analysis Of PSO Hybrid Algorithms For Feed-Forward Neural Networks Training
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Optimizing feedforward artificial neural network architecture
Engineering Applications of Artificial Intelligence
Particle Swarm Optimization of Neural Network Architectures andWeights
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Evolutionary Artificial Neural Network Design and Training for wood veneer classification
Engineering Applications of Artificial Intelligence
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm
IEEE Transactions on Neural Networks
Training neural nets with the reactive tabu search
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Artificial neural network (ANN) training is one of the major challenges in using a prediction model based on ANN. Gradient based algorithms are the most frequent training algorithms with several drawbacks. The aim of this paper is to present a method for training ANN. The ability of metaheuristics and greedy gradient based algorithms are combined to obtain a hybrid improved opposition based particle swarm optimization and a back propagation algorithm with the momentum term. Opposition based learning and random perturbation help population diversification during the iteration. Use of time-varying parameter improves the search ability of standard PSO, and constriction factor guarantees particles convergence. Since several contingent local minima conditions may happen in the weight space, a new cross validation method is proposed to prevent overfitting. Effectiveness and efficiency of the proposed method are compared with several other famous ANN training algorithms on the various benchmark problems.