研究目的
To design a new structure of SOFMs and the associated algorithm for efficient recognition of morphologies formed by messily grown nanowires.
研究成果
The as-proposed SOFMs can efficiently cluster and recognize the microstructure, quantity, and length of messily grown nanowires. The inter-layer connections between winning neurons significantly influence the relations between morphological microstructure and physical parameters of nanowires.
研究不足
The study relies on virtual training samples generated by simulations, which may not fully capture the complexity of real-world nanowire morphologies. The accuracy of predictions depends on the quality and representativeness of these virtual samples.
1：Experimental Design and Method Selection:
The study proposes multi-layer connected self-organizing feature maps (SOFMs) for clustering and recognizing nanowire morphologies. Virtual morphologies generated by Monte Carlo simulations serve as training samples.
2：Sample Selection and Data Sources:
Virtual training samples are generated to replace experimental samples, focusing on nanowire quantities, lengths, and morphologies.
3：List of Experimental Equipment and Materials:
The study involves simulations and does not specify physical equipment.
4：Experimental Procedures and Operational Workflow:
The neural network is trained with virtual samples to learn the relations between morphological microstructure and nanowire features. The trained network is then applied to experimental morphologies.
5：Data Analysis Methods:
The network's ability to cluster morphologies and recognize nanowire length and quantity is evaluated.
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