中文摘要：

针对系统输入为多元过程函数以及多维过程信号的信息处理问题，提出了多聚合过程神经元和多聚合过程神经元网络模型．多聚合过程神经元的输入和连接权均可以是多元过程函数，其聚合运算包括对多个输入函数的空间加权聚集和对多维过程效应的累积，可同时反映多个多元过程输入信号在多维空间上的共同作用影响以及过程效应的累积结果．多聚合过程神经元网络是由多聚合过程神经元和其它类型的神经元按照一定的结构关系组成的网络模型，按照输出是否为多元过程函数建立了前馈多聚合过程神经元网络的一般模型和输入输出均为过程函数的多聚合过程神经元网络模型，具有对多元过程信号输入输出关系的直接映射和建模能力．文中给出了一种基于多元函数基展开的梯度下降与数值计算相结合的学习算法，仿真实验结果表明了模型和算法对多元过程信号分类和多维动态过程模拟问题的适应性．

英文摘要：

Aimed at the information process problem that the system inputs are multivariate process functions and multi-dimension process signals, this paper proposes a kind of the multiaggregation process neuron and the multi-aggregation process neural networks model. The inputs and connection weights of multi-aggregation process neuron all can be multivariate process functions, and its aggregation operations include space weight congregation to many input functions and the cumulation of multi-dimension process effect, can simultaneity reflect many multivariate process input signals that effect together in multi-dimension space and the cumulation result of process effect. Multi-aggregation process neural networks are composed of multi-aggregation process neurons and other type neurons according to certain structure connection, in the light of whether the outputs are multivariate process functions or not, the general model of feedback multi-aggregation process neural networks and multi-aggregation process neural networks model which inputs and outputs are all process functions are founded, have the direct mapping and modelling ability to the input/output connection of multivariate process signals. A kind of learning algorithm based on the grads descending with numerical computation integrating which bases on multivariate function base expanding is proposed in this paper, and the simulation experiments results show the adaptability of models and algorithms to the multivariate process signal classification and the simulation problems of multi-dimension dynamic process.