By combining particle swarm optimization (PSO) and genetic algorithms (GA) this paper offers an innovative algorithm to\ntrain artificial neural networks (ANNs) for the purpose of calculating the experimental growth parameters of CNTs. The paper\nexplores experimentally obtaining data to train ANNs, as a method to reduce simulation time while ensuring the precision of\nformal physics models. The results are compared with conventional particle swarmoptimization based neural network (CPSONN)\nand Levenbergââ?¬â??Marquardt (LM) techniques. The results show that PSOGANN can be successfully utilized for modeling the\nexperimental parameters that are critical for the growth of CNTs.
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