摘要
针对瓦斯气体井下环境检测时易受干扰、硬件测量易产生漂移的问题,在建立神经网络红外瓦斯传感器补偿模型基础上提出小种群混沌粒子群算法(SPCPSO)对神经网络的部分参数进行优化。引入帐篷映射混沌算法来提高粒子群算法的遍历性,提高了算法的二次搜索精度。
A small population chaos particle swarm optimization algorithm（SPCPSO） is proposed to optimize the parameters of the neural network based on the neural network infrared gas sensor compensation model, which is easy to be disturbed when the gas gas underground environment is detected and drift is easily generated by the hardware measurement. The chaotic algorithm of tent map is introduced to improve the ergodicity of PSO and improve the secondary search accuracy of the algorithm.
出处
《煤炭技术》
北大核心
2017年第7期173-175,共3页
Coal Technology
关键词
小种群
粒子群
帐篷映射
混沌
small population
particle swarm
tent map
chaos
