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Neural emulator for nonlinear systems based on PSO algorithm: real-time validation
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DOI：10.1007/s10586-023-04166-9.png)
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Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (PSO) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to accelerate the convergence speed and to improve the precision degree. The obtained results are compared with those reached with an intelligent tuning strategy. An experimental validation of the new emulator adaptation is carried on chemical reactor. Efficiency of the proposed method is proved according to the obtained performances.
Keyword:
Neural emulation
PSO algorithm
Adaptive rate
Nonlinear systems
Experimental validation
Chemical reactor
期刊
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4.1
论文数:
5.1K
被引数:
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