专利详细信息

基于无迹卡尔曼神经网络的PM2.5浓度预测方法

文献类型：专利

专利类型：发明专利

是否失效：否

是否授权：否

申请号：CN201610457234.8

申请日：20160622

发明人：苏盈盈王晓峰贾威贾瑞楠

申请人：重庆科技学院

申请人地址：401331 重庆市沙坪坝区虎溪大学城重庆科技学院

公开日：20161116

公开号：CN106126909A

代理人：龙玉洪

代理机构：重庆为信知识产权代理事务所(普通合伙)

语　　种：中文

摘　　要：本发明提供了一种基于无迹卡尔曼神经网络的PM2.5浓度预测方法，包括如下步骤：获取PM2.5浓度的历史数据，基于无迹卡尔曼神经网络进行递推计算，利用MATLAB仿真软件编程，不断进行在线预测，不断修正权值和阈值，建立PM2.5浓度预测的动态演化模型，在得到动态演化模型和预测数据的基础上，建立图视化预警，以达到在数据预测后的预警作用。相较于基于BP神经网络建模而言，基于无迹卡尔曼神经网络建模在预测结果上更加的精准，误差更小。

主权项：1.一种基于无迹卡尔曼神经网络的PM2.5浓度预测方法，其特征在于，包括如下步骤：S1：获取PM2.5浓度的历史数据；S2：利用无迹卡尔曼神经网络进行递推运算，并通过不断地进行在线预测，循环修正神经网络的权值和阈值，实现对PM2.5浓度混沌时间序列的预测，以建立PM2.5浓度预测的动态演化模型：设有一个N层前向神经网络，每层神经元数为S_k(k＝1,2,…,N)，输入层为第一层，输出层为第N层，第k层神经元的连接权值为(i＝1,2,…,S_k-1，j＝1,2,…,S_k)，将神经网络的权值w、阈值b作为无迹卡尔曼滤波的状态变量，将神经网络的输出作为无迹卡尔曼滤波的测量变量，将神经网络中的所有权值和阈值组成状态向量： $<math> <mrow> <mi>X</mi> <mo>=</mo> <msup> <mrow> <mo>[</mo> <msubsup> <mi>w</mi> <mn>11</mn> <mn>1</mn> </msubsup> <mo>...</mo> <msubsup> <mi>w</mi> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mn>1</mn> </msubsup> <msubsup> <mi>w</mi> <mn>11</mn> <mn>2</mn> </msubsup> <mo>...</mo> <msubsup> <mi>w</mi> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mn>2</mn> </msubsup> <msubsup> <mi>b</mi> <mn>11</mn> <mn>1</mn> </msubsup> <msubsup> <mi>w</mi> <m</p><p class='subject'><strong>关键词：</strong>无迹动态演化浓度预测 <b class='type_ico_id'></b>神经网络 <b class='type_ico_id'></b>神经网络建模递推计算历史数据 <b class='type_ico_id'></b>软件编程数据预测预测结果预测数据 <b class='type_ico_id'></b>预警作用在线预测权值和建模 <b class='type_ico_id'></b>预警修正 </p><p class='class'><strong>IPC专利分类号：</strong>G06F19/00(20110101);G06N3/04(20060101)</p>
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基于无迹卡尔曼神经网络的PM2.5浓度预测方法

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