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文化与乡村旅游融合的认知诊断

Cognitive Diagnosis of Cultural and Rural Tourism Convergence.

作者信息

Liu Yanjuan

机构信息

School of Business Administration, Henan University of Animal Husbandry and Economy, Zhengzhou 450044, China.

出版信息

Transl Neurosci. 2019 Apr 23;10:19-24. doi: 10.1515/tnsci-2019-0004. eCollection 2019.

DOI:10.1515/tnsci-2019-0004
PMID:31098307
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6487916/
Abstract

Neural networks are widely used in the field of cognitive diagnosis. Cognitive diagnosis can diagnose the subjects' knowledge of cognitive attributes according to their responses, so as to obtain the specific cognitive status of the subjects and provide remedial measures. The studies on the convergence of cultural industry and tourism industry are emerging, but the theoretical system needs to be improved. The research on the convergence mechanism between cultural industry and tourism industry can complement each other on the basis of independent theoretical system, which establishes relationship between the two theoretical systems. Based on the adaptive neural network algorithm and from the perspective of blockchain, this study takes cultural industry and rural tourism industry as examples to diagnose the industry convergence of rural cultural industry and rural tourism industry development, which will further consolidate the theoretical basis for the convergence and development of tourism industry and cultural industry, as well as contribute to promoting development of industry convergence.

摘要

神经网络在认知诊断领域得到广泛应用。认知诊断可以根据受试者的回答来诊断他们对认知属性的掌握情况,从而获得受试者的具体认知状态并提供补救措施。关于文化产业与旅游业融合的研究不断涌现,但理论体系有待完善。文化产业与旅游业融合机制的研究能够在各自独立理论体系的基础上相互补充,建立起两者理论体系之间的联系。本研究基于自适应神经网络算法并从区块链的视角出发,以文化产业和乡村旅游业为例,对乡村文化产业与乡村旅游业发展的产业融合进行诊断,这将进一步巩固旅游业与文化产业融合发展的理论基础,也有助于推动产业融合发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/c685707e3c4a/tnsci-10-019-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/f32a5c38b505/tnsci-10-019-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/ccfa8b507c1f/tnsci-10-019-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/7940ac69d6cc/tnsci-10-019-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/41df74dafbf1/tnsci-10-019-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/ac814afa8d87/tnsci-10-019-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/c685707e3c4a/tnsci-10-019-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/f32a5c38b505/tnsci-10-019-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/ccfa8b507c1f/tnsci-10-019-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/7940ac69d6cc/tnsci-10-019-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/41df74dafbf1/tnsci-10-019-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/ac814afa8d87/tnsci-10-019-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dba9/6487916/c685707e3c4a/tnsci-10-019-g006.jpg

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A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.一种用于多速率网络工业过程控制的组合自适应神经网络和非线性模型预测控制。
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