Data Mining Models And Applications Of Time Series Based On Process Neural Networks

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.