自组织过程神经网络及其应用研究

针对与时间过程有关的模式分类问题，提出了一种自组织过程神经元网络模型．网络由输入层和竞争层组成，其输入和连接权可为与时间有关的函数，输入层结点与竞争层结点实行全互连接．网络提取输入函数所隐含的过程式模式特征，并对其进行自组织，在竞争层将分类结果表现出来．为简化计算，在输入空间中引入函数正交基，将输入函数和网络权函数表示为正交基的展开形式，利用基函数的正交性，使网络权函数的调整非时变化．给出了竞争学习和有教师示教两种学习算法，并以石油地质中沉积微相识别问题为例证明了模型和算法的有效性．

Research and Applications of Self-Organization Process Neural Networks

Aimed at the pattern classification problems relating to time process, a neural network model named self-organization process neural network is brought forward in this paper. The network consists of input layer and competition layer, and its input and link weights are functions relating to time. The nodes of input layer and competition layer link with each other totally. The network extracts the implicit pattern characters of input function and self-organizes them, and then represents the classification result at competition layer. In order to simplify computing, function orthogonal base is introduced into input space, and the input and weight functions are represented as the expansion form of orthogonal base. Using the orthogonality of base function, the adjustment of network weight coefficients can be made independent of time. The learning algorithms of competition learning and teacher demonstration are given. The effectiveness of the model and algorithms is proved by sedimentary faces identification in petroleum geologic.