Abstract
Particle swarm optimization algorithm (PSO) is applied to train artificial neural network (NN) to construct a neural network based on particle swarm optimization algorithm (PSONN). Then, PSONN is employed to construct a practical soft-sensor of gasoline endpoint of main fractionator of fluid catalytic cracking unit (FCCU). The obtained results indicate that soft-sensing model based on PSONN has better performance than soft-sensing model based on BPNN and the new method proposed by this paper is feasible and effective in soft-sensing modeling of gasoline endpoint.
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Keywords
- Neural Network
- Particle Swarm Optimization
- Artificial Neural Network
- Particle Swarm Optimization Algorithm
- Main Fractionator
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Chen, G., Yu, J. (2005). Particle Swarm Optimization Neural Network and Its Application in Soft-Sensing Modeling. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_86
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DOI: https://doi.org/10.1007/11539117_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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