摘要
过程神经元网络(PNN,Process Neural Networks)是传统神经元网络扩展到时间域上的一种新型人工神经网络模型。网络的输入与输出均可为依赖于时间(或过程)变化的连续函数。模型具有良好的非线性性质、强泛化能力和高容错性等特点。针对传统人工神经网络在解决时间序列数据挖掘问题时受到输入同步瞬时限制的问题,论文提出了一类基于过程神经元网络的时间序列数据挖掘模型和算法。这类模型在处理时间序列数据挖掘问题时,能够充分反映时间序列中实际存在的时间累积效应,其模型精度和泛化能力都高于传统人工神经元网络。因此,研究基于过程神经元网络的时间序列数据挖掘模型和算法对于解决时间序列数据挖掘问题具有很重要的意义。
论文首先介绍了时间序列数据挖掘的研究现状,分析了传统人工神经网络解决时间序列数据挖掘问题的局限性,并指出用过程神经元网络解决时间序列数据挖掘问题;结合离散过程神经元网络模型和双隐层过程神经元网络模型,论文提出了一类基于过程神经元网络的时间序列数据挖掘模型,即时间序列过程神经元网络挖掘模型和双隐层时间序列过程神经元网络挖掘模型,并给出了基于离散Walsh函数变换的学习算法,用于确定网络的连接权值和激励阈值。论文最后将基于过程神经元网络的时间序列数据挖掘模型应用于油田地质领域中的水淹层自动判别和沉积微相自动识别等时间序列数据挖掘实际问题。在水淹层自动判别实例中,先后用时间序列过程神经元网络挖掘模型和双隐层时间序列过程神经元网络挖掘模型对大庆油田葡区4口井的测井数据进行训练识别,结果显示双隐层时间序列过程神经元网络的收敛速度和模型精度高于时间序列过程神经元网络。双隐层时间序列过程神经元网络挖掘模型实际上可以看成是时间序列过程神经元网络挖掘模型的改进模型。在沉积微相自动识别实例中,网络学习训练之前,先采用了最小决策算法对标准模式类学习样本进行筛选,以提高网络的学习效率和适应能力。为了对比实验,将经过筛选的学习样本和未经过筛选的学习样本分别输入到相同结构和参数的双隐层时间序列过程神经元网络中,进行学习训练,对比结果显示在网络学习之前先对学习样本进行筛选的确提高了网络的收敛速度和学习效率。应用实例证明了模型和算法的有效性。
Process Neural Networks (PNN) are a type of novel Artificial Neural Networks models, which can be seen as the Artificial Neural Networks in time domain. Both Inputs and outputs of the networks could be continuous functions that are related to time or procedure. The models have good nonlinear properties, powerful generalization ability and strong fault tolerance. Aimed at the problems that traditional Artificial Neural Networks have some disadvantages to solve Data Mining problems of Time Series, a type of Data Mining models and algorithms of Time Series based on Process Neural Networks are proposed in this paper. The models could reflect the temporal accumulation effect of time serie when they are applied to solve Data Mining problems of Time Series, of which the precision and generalization ability are better than those of traditional Artificial Neural Networks. Theirfore, the research on Data Mining models and algorithms of Time Series based on Process Neural Networks is significant to solve Data Mining problems of Time Series.
Firstly, the paper introduces the current research status of Time Series Data Mining problems, analyzes the disadvantages of traditional Artificial Neural Networks to solve Data Mining problems of Time Series, and presents that Process Neural Networks could be used to solve Data Mining problems of Time Series. Secondly, combined Discrete Process Neural Networks with Process Neural Networks with Double Hidden Layers, a type of Data Mining models and algorithms of Time Series based on Process Neural Networks are proposed in this paper, which are Time Series Process Neural Networks and Time Series Process Neural Networks with Double Hidden Layers. The algorithms are based on discrete Walsh conversion, which are used to solve connection weights and activation thresholds of the networks. Lastly, The Data Mining models and algorithms of Time Series based on Process Neural Networks are applied in the oil field to solve the problems of flooding formation automatic identification and sedimentary facies automatic recognition.In the application of flooding formation automatic identification, Time Series Process Neural Networks and Time Series Process Neural Networks with Double Hidden Layers are applied to train and recognize the logging data of four development wells in the area of Xinshugang respectively. The results show that the convergence rate and precision of Time Series Process Neural Networks with Double Hidden Layers are better than those of Time Series Process Neural Networks. In fact, Time Series Process Neural Networks with Double Hidden Layers could be seen as the modified model of Time Series Process Neural Networks. In the application of sedimentary facies automatic recognition, the minimum decision algorithm is used to select learning samples of standard patterns before the training of networks to improve the learning efficiency and adaptability of the networks. In order to comparie with the experiment results, both the learning samples that are selected before and the learning samples that are not selected before are used to train and identify with the same Time Series Process Neural Networks with Double Hidden Layers. The results show that used the learning samples that are selected before could improve the learning efficiency and adaptability of the networks. The results of the applications are proved the effectiveness of models and algorithms.
引文
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