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- W3217020797 abstract "This paper presents a framework of Hopfield neural network crawler with information entropy and memristor model to design two novel methods for calculation of the relevance of hypertext link, which optimizes crawling strategy. Based on single-layer Hopfield neural network, adopting breadth-best-first crawling strategy, designing memristor-based activation function, and applying information entropy and cubic nonlinearity memristor model to calculate hyperlink weight are highlighted. Our experimental results on a series of Tibet development topics demonstrate the performance of our crawlers, which efficiently increase the topic-relevant similarity and achieve higher precision than other crawlers." @default.
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- W3217020797 date "2021-10-15" @default.
- W3217020797 modified "2023-09-23" @default.
- W3217020797 title "Improved Neural Network Crawler with Information Entropy and Memristor Model" @default.
- W3217020797 doi "https://doi.org/10.1109/icnc52316.2021.9608855" @default.
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