BP网络的一种改进学习方法

章分析了传统BP学习方法的缺陷，给出了一种改进的学习方法，并用非线性函数tg△x和(e^△x-1)代替传统的线性函数△x进行网络学习和参数调整．仿真表明，该算法能有效克服网络陷入局部极小的困境，并大大提高收敛速度．     

多层神经网络的一个快速算法

本文对文[4]提出的前馈式多层神经网络的单参数动态搜索（SPDS）算法进行了深入的分析，给出了实现快速一维搜索的两个方案，从而实现了多层神经网络更为快速的学习训练．     

一种新的概率神经网络有监督学习算法

提出一种新的PNN有监督学习算法:用学习矢量量化对各类训练样本进行聚类,对平滑参数σ和距离各类模式中心最近的聚类点构造区域,并采用遗传算法在构造的区域内训练网络,实验表明:该算法在分类效果上优于其它PNN学习算法。     

多时滞环状神经网络的稳定性

本文针对—个带自反馈的多时滞环状神经网络系统,给出了系统平凡解稳定与不稳定的条件,讨论了平凡解对应特征方程在不同参数条件下的正实部根的个数以及正实部根个数随参数变动的变化规律.     

三元环状神经网络的稳定性

该文针对一个带自反馈的多时滞环状神经网络系统,在整个参数空间中,分区域系统完整地讨论了系统平凡解稳定与不稳定的条件,并根据正实部特征根个数依参数变动的变化规律,给出了不同参数条件下平凡解的相应最大稳定区域边界,改进和完善了先前的结果.     

一类时滞神经网络模型的稳定性

利用Lyapunov泛函,讨论了一类时滞神经网络模型 τ_j≥0,i=1,2,…,n平衡态的稳定性,获得了几个充分条件.     

基于学习向量量化神经网络的水环境质量综合评价

针对水环境质量综合评价中指标权重确定方法的不足,利用学习向量量化(LVQ)神经网络具有的强大的非线性运算和相似特征聚类功能,提出一种基于学习向量量化(LVQ)神经网络的水质综合评价决策方法.将它应用于水质综合指标评价,为改进水质综合评价提供了一种简捷的分类评价方法.     

Practical Use of Distributions in Network Analysis

When modelling project networks under uncertainty, there are a variety of probability distributions from which to choose for activity-durations. From experience of risk analysis in practice, this paper offers some conclusions on how many and which parameters should be estimated, and which distributions should be used.     

Solving the AGV Problem via a Self-Organizing Neural Network

Automated Guided Vehicle (AGV)-based material handling systems (MHS) are used widely in Flexible Manufacturing Systems (FMS). The problem of AGV consists of the decisions and the operational control strategies of dispatching, routeing and scheduling of a set of AGVs under given system environments and operational objectives. One remaining challenge is to develop effective methods of AGV decisions for improved system productivity. This paper describes a prototype neural network approach for the AGV problem in an FMS environment. A group of neural network models are proposed to perform dispatching and routeing tasks for the AGV under conditions of single or multiple vehicles, and with or without time windows. The goal is to satisfy the transport requests in the shortest time and in a non-conflicting manner, subject to the global manufacturing objectives. Based on Kohonen's self-organizing feature maps, we have developed efficient algorithms for the AGVs decisions, and simulation results have been very encouraging.     

实时求解线性规划问题的原对偶神经网络(英文)

本文探讨了线性规划的原问题与对偶问题理论,并在此基础上可开发出一种用于在线求解线性规划的递归神经网络和应用于冗余机器手臂逆运动学的求解问题上.如,Tang等人开展的原对偶神经网络.但鉴于对偶理论的复杂性和多样性,该原对偶神经网络模型仅可以得到线性规划问题的可行解,而本文对该网络模型改进后可得到线性规划问题的最优解.仿真结果证实了这种改进模型在解决线性规划问题上的有效性、正确性和高效率.     

基于神经网络的SND近似计算

利用查表确定标准正态分布的函数值非常有限,这给工程应用带来很多不便。文章讨论了基于神经网络计算标准正态分布函数值的方法、数学原理、网络构造和学习过程。示例表明,计算简洁、方便,准确率能达到10^-6。     

具时滞的神经网络模型的非平凡周期解的全局存在性

本文考虑具时滞的n维神经网络模型.利用FDE的全局Hopf分支存在定理和Bendixson周期解不存在定理,给出该模型存在非平凡周期解的条件.     

Representing a Functional Curve by Curves with Fewer Peaks

In this paper, we study the problems of (approximately) representing a functional curve in 2-D by a set of curves with fewer peaks. Representing a function (or its curve) by certain classes of structurally simpler functions (or their curves) is a basic mathematical problem. Problems of this kind also find applications in applied areas such as intensity-modulated radiation therapy (IMRT). Let f\bf f be an input piecewise linear functional curve of size n. We consider several variations of the problems. (1) Uphill–downhill pair representation (UDPR): Find two nonnegative piecewise linear curves, one nondecreasing (uphill) and one nonincreasing (downhill), such that their sum exactly or approximately represents f\bf f. (2) Unimodal representation (UR): Find a set of unimodal (single-peak) curves such that their sum exactly or approximately represents f\bf f. (3) Fewer-peak representation (FPR): Find a piecewise linear curve with at most k peaks that exactly or approximately represents f\bf f. Furthermore, for each problem, we consider two versions. For the UDPR problem, we study its feasibility version: Given ε>0, determine whether there is a feasible UDPR solution for f\bf f with an approximation error ε; its min-ε version: Compute the minimum approximation error ε ^∗ such that there is a feasible UDPR solution for f\bf f with error ε ^∗. For the UR problem, we study its min-k version: Given ε>0, find a feasible solution with the minimum number k ^∗ of unimodal curves for f\bf f with an error ε; its min-ε version: given k>0, compute the minimum error ε ^∗ such that there is a feasible solution with at most k unimodal curves for f\bf f with error ε ^∗. For the FPR problem, we study its min-k version: Given ε>0, find one feasible curve with the minimum number k ^∗ of peaks for f\bf f with an error ε; its min-ε version: given k≥0, compute the minimum error ε ^∗ such that there is a feasible curve with at most k peaks for f\bf f with error ε ^∗. Little work has been done previously on solving these functional curve representation problems. We solve all the problems (except the UR min-ε version) in optimal O(n) time, and the UR min-ε version in O(n+mlog m) time, where m<n is the number of peaks of f\bf f. Our algorithms are based on new geometric observations and interesting techniques.     

一类变时滞离散Cohen-Grossberg神经网络模型周期解的存在性及其指数稳定性

利用重合度理论研究了一类变时滞的离散Cohen-Grossberg神经网络模型的周期解,并得到了模型周期解的全局指数稳定性的充分条件,推广了已有的结果,为神经网络的应用提供了重要的理论基础.最后给出一个例子进行数值模拟,数值模拟的结果更好地验证了结论.     

一类时滞三维离散神经网络模型的稳定性和分支

讨论了一类带时滞的离散三维神经网络模型.利用数学分析技巧,对线性化系统的特征根进行分析,获得了平衡点的局部稳定性及分支点,并利用规范型和中心流形理论得出了决定分支方向和稳定性的公式.     

一类变时滞离散Cohen-Grossberg神经网络模型周期解的存在性及其指数稳定性

利用重合度理论研究了一类变时滞的离散Cohen-Grossberg神经网络模型的周期解,并得到了模型周期解的全局指数稳定性的充分条件,推广了已有的结果,为神经网络的应用提供了重要的理论基础.最后给出一个例子进行数值模拟,数值模拟的结果更好地验证了结论.