1) multi-aggregation process neural networks
多聚合过程神经元网络
1.
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 multi-aggregation process neuron and the multi-aggregation process neural networks model.
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。
2) process neural networks
过程神经元网络
1.
A process neural networks model and its application to dynamic forecasting;
一种过程神经元网络模型及其在动态预测中的应用
2.
Process neural networks with time-varying inputs and outputs and learning algorithm;
一种时变输入输出过程神经元网络及学习算法研究
3.
Aimed at the problems of the time-varying information processing and the dynamic system modeling, two kinds of process neural network models,including the rational formula process neural networks and the process neural networks with time-varying inputs and outputs function,were built in this paper.
针对时变信息处理和动态系统建模等类问题,建立了输入输出均为时变函数的过程神经元网络和有理式过程神经元网络2种网络模型。
3) process neural network
过程神经元网络
1.
Research and application of process neural network with two hidden-layer based on expansion of basis function;
基于基函数展开的双隐层过程神经元网络及其应用
2.
A dynamic prediction method based on process neural networks is proposed for the process forecasting and prediction problem of dynamic system.
针对动态系统过程预测预报问题,提出了一种基于过程神经元网络的动态预测方法。
3.
A modular process neural network model based on multi-basis-functions is brought forward in this article,in which the spatial aggregation and temporal limited accumulation of discrete-time inputs are involved.
提出一类基于多种正交基函数的模块化过程神经元网络模型,它融入了多时变输入的空间聚合和作用域限制的时间累积,并采用多种正交基函数在较小网络规模的条件下保证系统各种输入输出的精度,应用混合隐含层综合考虑了系统多类型输入对系统的作用,并应用模块化级联的方式在一定程度上减小了网络的总体容量,从而提高了整个网络的学习速度。
4) multi-aggregation process neuron
多聚合过程神经元
1.
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 multi-aggregation process neuron and the multi-aggregation process neural networks model.
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。
5) Fuzzy process neural network
模糊过程神经元网络
6) structural formula process neural networks
分式过程神经元网络
1.
Aimed at the pattern classification and the system-modelling problem with complex time-varying signals that have singular values, a kind of structural formula process neural networks is proposed in this paper.
针对带有奇异值复杂时变信号的模式分类和系统建模问题,提出了一种分式过程神经元网络·该模型是基于有理式函数具有的对复杂过程信号的逼近性质和过程神经元网络对时变信息的非线性变换机制构建的,其基本信息处理单元由两个过程神经元成对偶组成,逻辑上构成一个分式过程神经元,是人工神经网络在结构和信息处理机制上的一种扩展·分析了分式过程神经元网络的连续性和泛函数逼近能力,给出了基于函数正交基展开的学习算法·实验结果表明,分式过程神经元网络对于带有奇异值时变函数样本的学习性质和泛化性质要优于BP网络和一般过程神经元网络,网络隐层数和节点数可较大减少,且算法的学习性质与传统BP算法相同
补充资料：神经元形态中枢神经系统内轴突髓鞘的形成示意图
李瑞端绘
[图]
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条