Abstract：Traditional probabilistic neural network(PNN) adopted localized and Gauss-styled operational function of Radial Basis Function that has better categorizing ability but exists the following limitations:① the number of neural cells of model increased if studying samples increased,which leads operational matrix to increase and makes it almost lose the ability of large data volume processing;② The weight from model to summation is fixed as constant that needs the number of all kinds of samples in studying sets to be equal,which affects the ability of receiving real data.In this reason,the paper introduced the Dynamic Probabilistic Neural Network (DPNN),which is different from traditional PNN in structure as follows:① using unequal weight to connect the model and summation,the weight is determined by probabilistic distribution of studying sample sets;②the number of different kinds of samples in vStudying sample sets (the number of neural cells lain in model for different kinds) can be unequal.The paper also introduced DPNN algorithm.The theoretic data tests showed the DPNN structure is characterized by rapid dynamic adjustment and studying convergence as well as strong ability of identifying categories.The 22 attributes of real data in G oilfield are selected as input vectors of network,the oil-bearing probabilistic distribution map is obtained by using categorized identification,which can provide the basis of predicting prospective oil/gas traps and distributing rule of oil/water.
徐旺林, 庞雄奇, 吕淑英, Michael R Berthold. 动态概率神经网络及油气概率分布预测[J]. 石油地球物理勘探, 2005, 40(1): 65-70.
Xu Wanglin, Pang Xiong-qi, Lu Shu-ying, Michael R Berthold. Dynamic probabilistic neural network and predicting probabilistic distribution of oil/gas. OGP, 2005, 40(1): 65-70.
