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A BP Neural Network Based on Improved Particle Swarm Optimization and its Application in Reliability Forecasting
| Content Provider | Semantic Scholar |
|---|---|
| Author | Li, Heqing Tan, Qing |
| Copyright Year | 2013 |
| Abstract | The basic Particle Swarm Optimization (PSO) algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow full play to the approximation capability of the function of BP neural network and overcome the main shortcomings of its liability to fall into local extreme value and the study proposed a concept of applying improved PSO algorithm and BP network jointly to optimize the original weight and threshold value of network and incorporating the improved PSO algorithm into BP network to establish a improved PSO-BP network system. This method improves convergence speed and the ability to search optimal value. We apply the improved particle swarm algorithm to reliability prediction. Compared with the traditional BP method, this kind of algorithm can minimize errors and improve convergence speed at the same time. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://maxwellsci.com/print/rjaset/v6-1246-1251.pdf |
| Alternate Webpage(s) | http://www.maxwellsci.com/print/rjaset/v6-1246-1251.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Accidental Falls Algorithm Approximation Approximation error Artificial neural network Biological Neural Networks Convergence (action) Generalization (Psychology) Iteration Maxima and minima Microsoft Lumia Network model Neural Network Simulation Nonlinear system Optimization problem Particle swarm optimization Population Parameter Projections and Predictions Software bug Vergence |
| Content Type | Text |
| Resource Type | Article |
