Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm
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Publication:387497
DOI10.1016/j.amc.2012.04.069zbMath1282.90248OpenAlexW2050546928MaRDI QIDQ387497
Hossein Moradian Sardroudi, Siti Zaiton Mohd Hashim, SeyedAli Mirjalili
Publication date: 23 December 2013
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2012.04.069
FNNneural networkmultilayer perceptronevolutionary algorithmgravitational search algorithmparticle swarm optimizationPSOlearning neural network
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Uses Software
Cites Work
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- BGSA: Binary gravitational search algorithm
- A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training
- An online gradient method with momentum for two-layer feedforward neural networks
- Approximating polynomial functions by feedforward artificial neural networks: Capacity analysis and design
- An adaptive conjugate gradient learning algorithm for efficient training of neural networks
- Multilayer feedforward networks are universal approximators
- GSA: A gravitational search algorithm
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