1引言泛函网络(Functional networks)是Castillo E在1998年提出的一种网络模型[1,2],是人工神经网络的一种有效扩展,它处理的是一般的泛函模型。
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A kind of functional network with single input and single output,as well as one with double inputs and single output,is designed as a basic functional network model.
设计了一类单输入单输出泛函网络与双输入单输出泛函网络作为构造层次泛函网络基本模型。
参考来源 - 层次泛函网络学习算法及其在时间序列分析中的应用Function network is a recently introduced extension of neural networks. It has certain advantages solving non-linear problems.
泛函网络是最近提出的一种对神经网络的有效推广,在处理非线性问题时有一定的优势。
参考来源 - 基于泛函网络的非线性回归预测模型及学习算法·2,447,543篇论文数据,部分数据来源于NoteExpress
泛函网络是最近提出的一种对神经网络的有效推广。
Functional network is a recently introduced extension of neural networks.
泛函网络是类似于人工神经网络的新型网络模型,是泛函方程的网络表达形式。
Functional network is new network model. It is similar to artificial neural network and is network expression of functional equation.
提出了一种多项式泛函网络运算新模型,来求解任意数域或环上多项式运算问题。
The new computing model of polynomial functional network is firstly proposed and the solving polynomials computing problem in arbitrary coefficients fields or ring is discussed.
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