scholarly journals Semi-supervised learning assisted particle swarm optimization of computationally expensive problems

2018 ◽  
Author(s):  
Chaoli Sun ◽  
Yaochu Jin ◽  
Ying Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Supervised Learning ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Expensive Problems ◽  
Computationally Expensive

scholarly journals Improved SpikeProp for Using Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Falah Y. H. Ahmed ◽  
Siti Mariyam Shamsuddin ◽  
Siti Zaiton Mohd Hashim
Keyword(s):  
Particle Swarm Optimization ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Learning Rule ◽  
Temporal Coding ◽  
Spiking Neurons ◽  
Learning Enhancement ◽  
Swarm Optimization ◽  
Spiking Networks

A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO) and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

scholarly journals Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Devin Akman ◽  
Olcay Akman ◽  
Elsa Schaefer
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Efficient Method ◽  
Epidemic Model ◽  
Particle Swarm ◽  
Computing Methods ◽  
Swarm Optimization ◽  
Computationally Expensive

Researchers using ordinary differential equations to model phenomena face two main challenges among others: implementing the appropriate model and optimizing the parameters of the selected model. The latter often proves difficult or computationally expensive. Here, we implement Particle Swarm Optimization, which draws inspiration from the optimizing behavior of insect swarms in nature, as it is a simple and efficient method for fitting models to data. We demonstrate its efficacy by showing that it outstrips evolutionary computing methods previously used to analyze an epidemic model.

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