Neural Network Application of Risk Identification on Innovative Enterprises in Yunnan Province

DOI

10.2991/aebmr.k.200708.059How to use a DOI?

Keywords

Innovation enterprise, risk identification, BP neural network component

Abstract

Innovation enterprise is an important component in the economic system of China, making indelible contribution for the flourishing and prosperity of economy of China. After a long-term development and improvement in many developed countries, the company major risk identification system has formulated a complete system gradually. The establishment method of risk identification system has been gradually transferred to mainly depending on capital market theory and computer information processing quantitative statistics from subjective scoring method and statistical discrimination method. The paper starts from the establishment of risk identification system for innovation enterprise, compares the risk identification methods to the innovation enterprise. The data sample is obtained through financial statement of the enterprise, questionnaire investigation and experts’ scoring method. Based on the prediction model of three-tier BP neural network, the risk identification indicators are learned and trained in order to get the importance of all variables and improve and optimize current risk identification evaluation system to the innovation enterprise at last.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jing Tie
AU  - Wenjing Zhao
AU  - Zhe Niu
PY  - 2020
DA  - 2020/07/09
TI  - Neural Network Application of Risk Identification on Innovative Enterprises in Yunnan Province
BT  - Proceedings of the 4th International Symposium on Business Corporation and Development in South-East and South Asia under B&R Initiative (ISBCD 2019)
PB  - Atlantis Press
SP  - 309
EP  - 312
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200708.059
DO  - 10.2991/aebmr.k.200708.059
ID  - Tie2020
ER  -