[发明专利]一种基于神经元网络的跨临界二氧化碳热泵系统排气压力的控制方法

权利要求书

1.一种基于神经元网络的跨临界二氧化碳热泵系统排气压力的控制方法，其特征在于，包括：

采集跨临界二氧化碳热泵系统运行中的环境温度T_air、蒸发器盘管温度T_e、气体冷却器的出口温度T_gc,out、热泵出水温度T_water,out，代入公式(1)中，计算获得压缩机最优排气压力P_opt；根据计算获得的压缩机最优排气压力P_opt，控制跨临界二氧化碳热泵系统中电子膨胀阀的开度，使压缩机的排气压力达到最优排气压力P_opt；

其中：P_opt——压缩机最优排气压力/MPa；

T_e——蒸发器盘管温度/℃；

T_air——环境温度/℃；

T_gc,out——气体冷却器的出口温度/℃；

T_water,out——热泵出水温度/℃；

W_ij——输入层到隐含层的权值；

w_i——隐含层到输出层的权值；

公式(1)通过以下方法获得：

1)采集三台机组7904组跨临界二氧化碳热泵系统的运行数据，获得数组；

2)建立PSO-BP神经网格，并获得PSO-BP神经网络的权值和阈值；

3)将步骤1)处理后的数组和步骤2)获得的神经网络的权值和阈值的数组代入PSO-BP神经网格求得神经网络的确切表达式为公式(1)；

步骤2)中，PSO-BP神经网格的结构选择3层结构；3层结构包括输入层，隐含层和输出层各一个；

PSO-BP神经网络的输入层和隐含层之间的节点传递函数采用了正切S型传递函数tansig；隐含层和输出层之间的节点传递函数为Purelin函数；训练函数选择梯度下降法BP算法训练函数trainlm，学习网络函数选择BP学习规则learndm，网络学习选择带动量项的BP学习规则learngm；

PSO-BP神经网络的权值和阈值的数组表为：

2.1)输入层到隐含层的权值：

W_ij＝{-1.373645179,-0.171434748,0.180450729,-0.419344707,-1.011174395,-1.095370306,-0.830633156,-2.375743856,-0.257030363,0.803925612,0.444660281,-0.956847463,-1.302595066,0.60939121,1.44270544,0.896090847,-0.256660406,1.071666471,1.091072963,-0.137175234,0.825383828,-0.554595693,1.333515141,-1.407159615,-1.071725234,-0.805831555,1.131061235,0.357316579,-0.903785699,0.663157186,-0.331196959,0.742801954,-0.610567156,2.525753297,0.243592494,0.709136427,-1.105587445,-0.72669056,1.346842937,0.751014569,0.952272284,0.177353584,1.061300908,0.875134325,0.128631765,1.449397231,0.223057781,-0.728439985,-1.089126606,-1.385787842,2.163516832,0.017385835,-0.254999419,1.282306975,-0.973242962,-1.156798981,-0.16573785,1.239275104,-0.86799971,-0.567617228,-0.439380446,-1.314739974,0.912920472,-0.645901465,-1.19571486,-1.741144827,-0.661052052,-0.565010726,-0.422493372,-0.579636988,0.960136387,0.771403746,0.755393263,-0.328503118,-1.271603343,0.798993956,0.438676136,1.09133534,-0.425173762,-1.134201289,-0.444948685,-1.039746691,0.35972868,1.134079237,-0.290332065,0.407184456,-1.206843505,0.592616827,-0.19963379,-0.861030702,0.785647446,0.706934095,-0.652098923,-0.883946838,0.849861688,0.329638324}；

2.2)输入层到隐含层的阈值

θ_i＝{0.257558476,-0.89355435,-0.930294354,0.620820834,-0.505408308,0.323818441,2.134062922,0.698001067,-0.692487021,-0.280434446,-0.379538764,0.042341748,-0.793686679,-0.280328752,-0.076848832,2.427111937,-0.111656369,1.334433533,-0.003113182,-0.958630098,-0.302250969,0.477703521,-0.050519198,0.308304082}；

2.3)隐含层到输出层的权值

w_i＝{1.069690833,0.705072106,-1.130932743,0.053217769,-0.101927472,-0.356915054,-2.359099898,0.387526948,1.132651956,-0.798591685,-0.261955516,-1.402381137,0.798599759,0.866208212,1.387519681,1.236713142,1.700995412,1.576768087,1.341827887,-0.062136221,-0.150827572,2.942764548,-0.391359771,-1.067339334}；

2.4)隐含层到输出层的阈值

a₁＝{-1.198399403}。