Hopfield networks for identification of delay differential equations with an application to dengue fever epidemics in Cuba

This work is aimed at proposing an algorithm, based upon Hopfield networks, for estimating the parameters of delay differential equations. This neural estimator has been successfully applied to models described by Ordinary Differential Equations, whereas its application to systems with delays is a novel contribution. As a case in point, we present a model of dengue fever for the Cuban case, which is defined by a delay differential system. This epidemiological model is built upon the scheme of an SIR (susceptible, infected, recovered) population system, where both delays and time-varying parameters have been included. The latter are thus estimated by the proposed neural algorithm. Additionally, we obtain an expression of the Basic Reproduction Number for our model. Experimental results show the ability of the estimator to deal with systems with delays, providing plausible parameter estimations, which lead to predictions that are coherent with actual epidemiological data. Besides, when the Basic Reproduction Number is computed from the estimated parameter values, results suggest an evolution of the epidemic that is consistent with the observed infection. Hence the estimation could help health authorities to both predict the future trend of the epidemic and assess the efficiency of control measures.     

Kosko型双向联想记忆与Hopfield神经网络的关系

深入理解K0sko型双向联想记忆(BAM)网络是学习使用其它双向联想记忆的基础.如果我们按照传统的方法介绍BAM网络,学生很容易产生两个疑问:能量函数为什么是定义的?BAM网络与Hopfield网络的内在联系是什么?本文在说明Kosko型BAM网络就是一种特殊的Hopfield网络后,从Hopfield网络的能量函数出...     

一维温度场的神经网络实现方法

直接从汽轮机转子的一维热传导方程出发，研究了汽轮机转子径向上的危险截面的一维温度场与Hopfield神经网络的相似机理，提出了应用连续Hopfield神经网络实现转子一维温度场的实时监测方法。通过传真可以看出，此种方法测量速度快，测量精度高，转子热时间常数与Hopfield神经网络的电时间常数近似相等，Hopfield神经网络模拟测量结果与转子的温度变化规律较一致。     

DECISION OF OPTIMAL HOPFIELD NEURAL NETWORK MODEL OF COMBINATIONAL CIRCUITS

By use of Hopfield model and basis solution of homogeneous linear equations which are established in accordance with consistent state, a practical decision method for the existence of optimal Hopfield model of combinational circuits is provided. Finally, an example is given.     

A robust boundary-based object recognition in occlusion environment by hybrid Hopfield neural networks

This paper presents a new method of occluded object matching for machine vision applications. The current methods for occluded object matching lack robustness and require high computational effort. In this paper, a new Hybrid Hopfield Neural Network (HHN) algorithm, which combines the advantages of both a Continuous Hopfield Network (CHN) and a Discrete Hopfield Network (DHN), will be described and applied for partially occluded object recognition in a multi-context scenery. The HHN proposed as a new approach provides great fault tolerance and robustness and requires less computation time. Also, advantages of HHN such as reliability and speed will be discussed.     

Biologically Plausible Associative Memory: Continuous Unit Response + Stochastic Dynamics

A neural network model of associative memory is presented which unifies the two historically more relevant enhancements to the basic Little-Hopfield discrete model: the graded response units approach and the stochastic, Glauber-inspired model with a random field representing thermal fluctuations. This is done by casting the retrieval process of the model with graded response neurons, into the framework of a diffusive process governed by the Fokker-Plank equation, which leads to a Langevin system describing the process at a microscopic level, while the time evolution of the probability density function is governed by a multivariate Fokker Planck equation operating over the space of all possible activation patterns. The present unified approach has two notable features: (i) greater biological plausibility and (ii) ability to escape local minima of energy (associated with spurious memories), which makes it a potential tool for those complex optimization problems for which the previous models failed.     

基于Hopneld网络的图的最大团和最大独立集算法

本文应用Hopfield网络,系统地研究了图的最大团和最大独立集问題,通过建立相应的数学理论,改进了这方面已有的工作,并进行了模拟实验,给出了实验研究的结果。     

具有时滞的高阶Hopfield型神经网络的稳定性

通过Lyapunov泛函的方法，对具有时滞的高阶连续型Hopfield神经网络平衡点的稳定性进行分析，利用Razumikhin定理得到平衡点全局一致渐近稳定的时滞相关与时滞无关充分条件。     

房地产投资风险稳定性实证分析

结合武汉房地产市场的实际,用一个多时滞神经网络系统反映武汉市房地产投资变量的运动轨迹。通过求解或仿真的过程模拟出微分方程的轨道曲线,认为其前期发展虽说势头迅猛,但目前的投资指标如经济附加值等都明显处于非正常高位,短期内虽说经济附加值会延续一定时间的上升状态,但其波动幅度也较大,稳定性较差,风险也不小。     

基于量子遗传算法优化Hopfield 神经网络军事训练效果评价

新兵入伍训练是提升战斗力的基础,对新兵入伍训练成绩进行科学评价,将入伍训练19个指标作为评估因子,选取8个班平均训练成绩进行模糊聚类分析,按照得分情况制定了4个等级的划分方法,提出了一种基于量子遗传算法优化离散型Hopfield神经网络的评估模型,然后将待分级的4名士兵训练成绩进行Hopfield编码评估,并用实例对模型进行验证,为基层部队强化智能化军事训练方法手段提供了理论依据.