Proceedings of the 2016 International Conference on Economics and Management Innovations

Psychological Assessment Data Processing Model Based on Neural Network Theory

Authors
Xiaowang Chen
Corresponding Author
Xiaowang Chen
Available Online July 2016.
DOI
10.2991/icemi-16.2016.43How to use a DOI?
Keywords
Neural networks, psychological evaluation, data processing, Service Component Architecture
Abstract

Neural networks are real rough neural network simulation, it is fault tolerant, anti-interference, high efficiency and other characteristics, with learning, memory and other cognitive functions powerful and therefore suitable for simulation of psychology in many areas. In this paper, the data from College Students Mental for the study, based on SCA (Service Component Architecture) technology to complete the establishment and management platform, based on theoretical knowledge of neural networks for processing the data, thus completing the psychological evaluation data models, different psychological data processing method evaluation provides a theoretical and technical research platform.

Copyright
© 2016, 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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Volume Title
Proceedings of the 2016 International Conference on Economics and Management Innovations
Series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
10.2991/icemi-16.2016.43
ISSN
2352-538X
DOI
10.2991/icemi-16.2016.43How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Xiaowang Chen
PY  - 2016/07
DA  - 2016/07
TI  - Psychological Assessment Data Processing Model Based on Neural Network Theory
BT  - Proceedings of the 2016 International Conference on Economics and Management Innovations
PB  - Atlantis Press
SP  - 215
EP  - 218
SN  - 2352-538X
UR  - https://doi.org/10.2991/icemi-16.2016.43
DO  - 10.2991/icemi-16.2016.43
ID  - Chen2016/07
ER  -