暂态混沌神经网络算法在矿井通风网络风量优化中的应用  

Application of transient chaotic neural network algorithm to optimization of mine ventilation network

作　　者：郝晓弘[1] 王永奇[1] 王莉[1] 

机构地区：[1]兰州理工大学电气工程与信息工程学院,甘肃兰州730050

出　　处：《兰州理工大学学报》2012年第1期71-74,共4页Journal of Lanzhou University of Technology

基　　金：甘肃省科技支撑计划项目(2008GS01878)

摘　　要：针对传统矿井通风网络解算方法的缺陷,提出一种新的暂态混沌神经网络的解算方法,利用混沌变量在混沌运动过程中所具有的遍历性、随机性来寻找全局的最优解,克服陷入局部极小的趋势.以通风总能耗最低为目标函数建立通风网络优化的数学模型,应用暂态混沌神经网络算法对一个简单通风网络的优化模型进行求解.实验结果表明:优化后通风系统总能耗降低了2.63kW,节能率大约为3.78%.Aimed at the defect of solution method for traditional mine ventilation network, a new method was proposed for the transient chaos neural network. Globally optimal solution for this network could be found by using the ergodicity and randomness of chaotic variables in chaos movement process. The tenden- cy to fall into local minimum was overcome. By taking the lowest total energy consumption of the ventila- tion as the objective function, a mathematic model for ventilation network optimization was established and then the transient chaotic neural network algorithm was used to solve the optimization model for a simple ventilation network. The result showed that the overall energy consumption of the optimized ventilation system was reduced by 2. 63 kW, with a energy-saving rate was about 3. 78%.

关 键 词：TCNN 矿井通风网络 优化模型 节能 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]