Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization
2013 ◽
Vol 475-476
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pp. 956-959
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Keyword(s):
In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.
2020 ◽
Vol 13
(1)
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pp. 98-112
2011 ◽
Vol 63-64
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pp. 106-110
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2012 ◽
Vol 4
(3)
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pp. 181-188
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2013 ◽
Vol 760-762
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pp. 2018-2022