Earticle

초록

영어

As a new entertainment and social way, online games now have a huge and increasing user group, so it is of great significance to identify the data stream of online games. Using the excellent nonlinear fitting ability of BP neural network and the advantages of global search of genetic algorithm, the initial weights and thresholds of BP neural network are optimized, and the BP neural network model optimized by genetic algorithm is established. The muti-dimensional input information is proposed to identify online game data streams. Through the experimental simulation, it shows that the selected muti-dimensional information and the established model can be well applied to online game stream recognition.

목차

ABSTRACT
1. Introduction
2. BP neural network optimized by genetic algorithm
2.1 Basic principles of genetic algorithm
2.2 genetic algorithm optimizes BP neural network algorithm model
3. Network game data stream recognition model
3.1 data sources
3.2 data quantification
3.3 Identification model
4. Experimental analysis
4.1 parameter setting
6. Conclusions
References

키워드

저자

• Daniel James [ Association of Scientists, Developers and Faculties 483, Green Lanes, Enfield, London N13 4BS, England, United Kingdom ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793