1) discrete neural network
离散神经网络
1.
A generalized discrete neural network for solving maximal independent set (MIS) problem is designed.
构造了一种用于最大独立集 (MIS)问题求解的广义离散神经网络模型 (GDHN) 。
2.
The performance of discrete neural network is considered when it is applied to combinatorial optimization problem with inequalities constraints,especially Knapsack problem is used to illustrate the idea we have proposed.
考虑在求解带有不等式约束的组合优化问题时 ,离散神经网络的设计及其性能分析。
3.
The specific description method of neurons based on discrete neural network definitions was put forward.
借助离散神经网络概念提出了集成系统中的特定神经元表示方法,给出了特定神经网络表达形式。
2) discrete-time neural networks
离散神经网络
1.
Exponential stability of discrete-time neural networks with multiple delays;
多重时滞离散神经网络的指数稳定性
2.
Synchronization of delayed discrete-time neural networks
时滞离散神经网络的同步控制
3) Discrete neural networks
离散神经网络
1.
Analysis of Dynamic Properties of Difference Equations and Discrete Neural Networks;
差分方程及离散神经网络的动力学性质分析
4) Discrete-time neural network
离散神经网络
1.
The problem of exponential stability and robust stability for a class of discrete-time neural network with time-varying delay is investigated.
研究了一类时滞离散神经网络指数稳定及鲁棒稳定问题。
5) discrete-time Hopfield neural networks
离散Hopfield神经网络
1.
Stability analysis of discrete-time Hopfield neural networks;
离散Hopfield神经网络的稳定性分析
2.
This paper makes a unified representation for several types of discrete-time Hopfield neural networks appearring in the literature currently with McCulloch-Pitts model which neuron acti- vation function is a strict threshold function and, hence, provides a systematic and clarified idea for the analysis and synthesis of these networks.
本文用神经元的激励函数为严格阈值函数的McCulloch-Pitts模型对目前出现于文献中的几种离散Hopfield神经网络作了统一描述,从而给这些网络的分析和综合提供了一条系统清晰的思路。
6) discrete hopfield neural networks
离散Hopfield神经网络
1.
With the aid of graph theory, the stability of discrete Hopfield neural networks with diagonal block matrix of the connection weight matrix is mainly studied in this paper.
以图论为工具 ,主要对连接权矩阵为对角分块形式的离散Hopfield神经网络的稳定性进行了研究。
补充资料：离散时间周期序列的离散傅里叶级数表示
(1)
式中χ((n))N为一离散时间周期序列，其周期为N点，即
式中r为任意整数。X((k))N为频域周期序列，其周期亦为N点，即X(k)=X(k+lN)，式中l为任意整数。
从式(1)可导出已知X((k))N求χ((n))N的关系
(2)
式(1)和式(2)称为离散傅里叶级数对。
当离散时间周期序列整体向左移位m时,移位后的序列为χ((n+m))N，如果χ((n))N的离散傅里叶级数(DFS)表示为,则χ((n+m))N的DFS表示为
式中χ((n))N为一离散时间周期序列，其周期为N点，即
式中r为任意整数。X((k))N为频域周期序列，其周期亦为N点，即X(k)=X(k+lN)，式中l为任意整数。
从式(1)可导出已知X((k))N求χ((n))N的关系
(2)
式(1)和式(2)称为离散傅里叶级数对。
当离散时间周期序列整体向左移位m时,移位后的序列为χ((n+m))N，如果χ((n))N的离散傅里叶级数(DFS)表示为,则χ((n+m))N的DFS表示为
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