Abstract：The high storage and computing cost limits the application of neural network model in low computing power platform. To overcome the above shortages, we proposed two combined probability optimization algorithms to combine multiple light neural networks in a continuous time window, which can significantly reduce computing load under similar accuracy. The proposed scheme gives a general way for related algorithm on embedded hardware. To verify its effectiveness, a group of experiment was executed on the pain expression recognition with continuous peak values. In comparison to other conventional algorithms, the model complexity, computing load and storage of proposed scheme decrease obviously under consistent accuracy.
李炎, 李宪, 杨明业, 孙国庆. 基于概率优化的神经网络模型组合算法[J]. 复杂系统与复杂性科学, 2022, 19(3): 104-110.
LI Yan, LI Xian, YANG Mingye, SUN Guoqing. Neural Network Model Combination Algorithm Based on Probability Optimization. Complex Systems and Complexity Science, 2022, 19(3): 104-110.
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