Game Flow Recognition Based on BP Neural Network and Optimized Genetic Algorithm
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (15)
- SHI Yongchao, "Research and Application of Acceleration Method for Online Games Based on QoS", Computer Applications and Software, 3, pp.16~19 (2015)
- YIN Yixing, "Study on the Impact of Mobile O nline Game Customer Experience on Customer Loyalty", pp.2015
- ZHOU Rui, "Characteristic Analysis and Recognition of Online Game Flow", Computer Engineering and Application, (23), pp.135~141 (2016)
- Raman M R G, "Hyper graph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems", Neural Networks, 92, pp.89~97 (2017)
- REN Haoran, "BP neural network stock price prediction model based on adaptive genetic algorithm optimization", pp.2017
- FAN Wei, "BP neural net⁃work creep prediction of piezoelectric ceramics optimized by genetic algorithm", Journal of Electrical Machinery and Control, 07, pp.95~100 (2018)
- Tahereh Azari, "Modeling the Neumans well function by an artificial neural network for the determination of unconfined aquifer parameters", Computational Geosciences, 22(4) (2018)
- Chun Yan, "Improved adaptive genetic algorithm for the vehicle Insurance Fraud Identification Model based on a BP Neural Network", pp.817~ (2020), Elsevier B.V,
- Guohui Zhang, "An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints", pp.54~ (2020), Elsevier B.V
- Tohru Nitta, "Hyperbolic Gradient Operator and Hyperbolic Back-Propagation Learning Algorithms", IEEE Transactions on Neural Networks and Learning Systems, 29(5), pp.1689~1702 (2018)
- Kim J, "Long short term memory recurrent neural network classifier for intrusion detection", 2016 International Conference on Platform Technology and Service (Plat Con),, pp.1~5 (2016), IEEE
- Cerrada M, "Fault diagnosis in spur gears based on genetic algorithm and random forest", Mechanical Systems and Signal Processing, 70, pp.87~103 (2016)
- Alom M Z, "Intrusion detection using deep belief networks", Aerospace and Electronics Conference, pp.339~344 (2016), IEEE
- Staudemeyer R C, "Applying long short-term memory recurrent neural networks to intrusion detection", South African Computer Journal, 56(1), pp.136~154 (2015)
- Agarap A F M, "A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM)for Intrusion Detection in Network Traffic Data", Proceedings of the 2018 10th International Conference on Machine Learning and Computing, pp.26~30 (2018), ACM