【作者】 李盼池；

【导师】 许少华；

【作者基本信息】 大庆石油学院 ， 计算机应用技术， 2004， 硕士

【摘要】 过程神经元网络是根据生物神经系统信息处理机制并结合实际问题的应用背景提出的一种新的人工神经网络模型。网络的输入输出可为过程或时变函数。过程式输入放宽了传统神经元网络模型对输入的同步瞬时限制，是传统神经元网络在时间域上的扩展，是更一般化的人工神经元网络模型。研究过程神经元网络模型的拓扑结构，函数逼近性质，学习算法等具有十分普遍的意义。 论文在理论和模型部分，概述了过程神经元的概念和过程神经元网络基本模型，阐述了基展开和投影组合两类过程神经元网络模型，证明了两种模型的等价性。构建了径向基、自组织、并联三类前馈型过程神经网络模型，研究了反馈型过程神经元网络的拓扑结构。在过程神经元网络的算法研究中，应用函数空间正交基的概念，可将积分算子变换为求和算子，从而有效避免了繁杂的时域聚合运算。分析了三角函数基及沃尔什函数基两类常用基函数的性质，并针对投影组合模型给出了基于权函数正交基展开的学习算法。研究了前馈过程神经网络基于离散沃尔什变换的学习算法，证明了算法的有效性；给出了反馈型过程神经元网络详细的算法推导过程，仿真实验证明了网络加入反馈后对加速收敛的有效性。论文最后结合油田开发中具体实际问题，给出了过程神经网络在复杂水淹层识别中的应用。更多还原

【Abstract】 Procedure Neural Networks (PNN) is a novel artificial neuron Networks model. It is based on information processing pattern of biological neural system and application background of practical matters. Both Input and output of the networks are cither procedures or functions. Procedure-type’s inputs to networks relax synchronization instantaneous limit on inputs in the traditional neural network models. So, it could be seen that the structures research, function approximation properties and learning algorithms of procedure neural network models is quite significant.In the theory and model section of this paper, the concepts of procedure neurons and procedure neural networks are presented. Two sorts of models of procedure neural networks are shown, they are expanded on base functions model and projection compounding model. The equivalence of two models is proved. Three sorts of feed forward procedure neural network structure is expatiated, they are radial base PNN, self-organizing PNN and parallel connection PNN. The feed back procedure neural network structure is researched. In the course of research of PNN algorithm, Applying the concept of vertical base function, integral operation is convert to additive operation and complicated aggregation in time field is avoided. Two sorts of general base function property is analyzed, they are trigonometric function and walsh function. Arming at projection compounding model, a learning algorithm base on weight function expanded on certain base function is proposed. The algorithm based on discrete walsh conversion is researched, and the validity of algorithm is proved. The elaborate algorithm of feedback PNN is presented. The simulation experience proved availability of the model on accelerating convergence. In the last section of paper, arming at practical matters in oil field exploitation, the application of PNN in the complex water flooded layer identification is shown.更多还原