极限学习脊波过程神经网络及应用  

Extreme Learning Ridgelet Process Neural Network and Application

作　　者：刘志刚[1] 许少华 肖佃师 杜娟[1] LIU Zhi-gang;XU Shao-hua;XIAO Dian-shi;DU Juan(School of Computer and Information Technology, Northeast Petroleum University Daqing Heilongjiang 163318;College of Computer Science and Engineering, Shandong University of Science and Technology Qingdao Shandong 266590;Institute of Unconventional Oil & Gas and New Energy, China University of Petroleum Qingdao Shandong 266580)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]山东科技大学计算机科学与工程学院,山东青岛266590 [3]中国石油大学(华东)非常规油气与新能源研究院,山东青岛266580

出　　处：《电子科技大学学报》2019年第1期110-116,共7页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(61170132;41330313);黑龙江省自然科学基金(F2015021)

摘　　要：为提高页岩的岩性识别精度,首先针对测井曲线连续变化、突变频繁的信号特征,利用脊波变换作为过程神经元的激励函数,提出一种脊波过程神经网络模型;其次通过Ada Boost的动态调整机制迭代调整模型和样本集权重,利用多个弱分类器的线性加权构建强分类器;最后为提高AdaBoost中的每个脊波过程神经网络模型的学习速度,提出一种基于满秩分解的极限学习算法,通过Moore-Penrose广义逆求解隐层输出权值。仿真实验以A区的B1井和B2井为例进行岩性识别,通过对比分析验证方法的有效性,识别效果优于其他过程神经网络模型,准确率最高可达90%左右。To improve the accuracy about shale lithology identification,this paper proposes a ridgelet process neural network(RPNN)and selects the ridgelet transform as the active function of process neuron according to the signal characteristic of continuous and frequent variation about logging curve.RPNN uses the dynamic mechanism of AdaBoost to adjust iteratively weights of the models and the sample.In order to improve the RPNN learning speed in AdaBoost,an extreme learning algorithm based on full rank decomposition is proposed.The proposed method was applied to the lithology identification for B1 and B2 well in A area and its effectiveness was tested through comparison and analysis.The results show that the lithology identification result of RPNN is better than other process neural networks and the recognition accuracy of RPNN is up to about 90 per cent.

关 键 词：极限学习 广义逆 岩性识别 过程神经网络 脊波变换 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.4[自动化与计算机技术—控制科学与工程]