Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
A split-step PSO algorithm in prediction of water quality pollution
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
A Group Selection Evolutionary Extreme Learning Machine approach for Time-Variant Neural Networks
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
An improved extreme learning machine based on particle swarm optimization
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Hybrid soft computing systems for reservoir PVT properties prediction
Computers & Geosciences
A modified artificial fish swarm algorithm for the optimization of extreme learning machines
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A multi-objective micro genetic ELM algorithm
Neurocomputing
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A new off-line learning method of single-hidden layer feed-forward neural networks (SLFN) called Extreme Learning Machine (ELM) was introduced by Huang et al. [1, 2, 3, 4] . ELM is not the same as traditional BP methods as it can achieve good generalization performance at extremely fast learning speed. In ELM, the hidden neuron parameters (the input weights and hidden biases or the RBF centers and impact factors) were pre-assigned randomly so there may be a set of non-optimized parameters that avoid ELM achieving global minimum in some applications. Adopting the ideas in [5] that a single layer feed-forward neural network can be trained using a hybrid approach which takes advantages of both ELM and the evolutionary algorithm, this paper introduces a new kind of evolutionary algorithm called particle swarm optimization (PSO) which can train the network more suitable for some prediction problems using the ideas of ELM.