Parametric identification of robotic systems with stable time-varying Hopfield networks

In this work, a novel method for on-line identification of non-linear systems is proposed based upon the optimisation methodology with Hopfield neural networks. The original Hopfield model is adapted so that the weights of the resulting network are time-varying. A rigorous analytical study proves that, under mild assumptions, the estimations provided by the method converge to the actual parameter values in the case of constant parameters, or to a bounded neighbourhood of the parameters when these are time-varying. Time-varying parameters, often appearing in mechanical systems, are dealt with by the neural estimator in a more natural way than by least squares techniques. Both sudden and slow continuous variations are considered. Besides, in contrast to the gradient method, the neural estimator does not critically depend on the adjustment of the gain. The proposed method is applied to the identification of a robotic system with a flexible link. A reduced output prediction error and an accurate estimation of parameters are observed in simulation results.This is a considerably extended version of a paper presented at the conference on Engineering Applications of Neural Networks (EANN), held in September 2003 at Málaga, Spain.     

Finite Memory Loading in Hairy Neurons

This paper presents a method to expand the basins of stable patterns in associative memory. It examines fully-connected associative memory geometrically and translate the learning process into an algebraic optimization procedure. It finds that locating all the patterns at certain stable corners of the neurons’ hypercube as far from the decision hyperplanes as possible can produce excellent error tolerance. It then devises a method based on this finding to develop the hyperplanes. This paper further shows that this method leads to the hairy model, or the deterministic analogue of the Gibb’s free energy model. Through simulations, it shows that this method gives better error tolerance than does the Hopfield model and the error-correction rule in both synchronous and asynchronous modes.     

随机Hopfield神经网络的定量分析

该文探讨了实际使用Hopfield神经网络(HNN)时噪声的影响。由于噪声的客观存在,我们首先证明了随机Hopfield神经网络(SHNN)轨道的期望关于时间是一致有界的。之后,为了实际设计神经网络的需要,我们对含有噪声的HNN和与其对应的一般HNN之间随机输入误差的估计进行了研究。利用所得的结论,我们可以对设计空间进行控制,使得所设计的网络满足我们希望获得的各种性能要求。     

一类离散Hopfield网络吸引状态的特征分析

该文首先讨论了超立方体图所对应的连接矩阵的特征向量,进而深入系统地分析了以n维超立方体为大规模局域连接模型的离散Hopfield网络的吸引特性之一,稳定吸引状态的位置、数量及其分布。研究结果表明,网络连接权矩阵的特征向量及其拼接向量均为网络的吸引子或吸引环,且其在网络状态空间中具有均匀对称的分布格局。     

基于对偶四元数的单目视觉目标位姿测量

目的在机器人视觉应用领域中,为控制机器人能够完成焊接、搬运、跟踪等任务,需要确定摄像机与目标之间的相对位姿关系,提出一种目标位姿测量方法。方法利用单摄像机获取目标特征,坐标变换参数表示为对偶四元数的形式,同时计算旋转矩阵和平移向量,构建位置向量和方向向量的测量值与模型值之间的误差方程,利用Hopfield神经网络实现拉格朗日乘子法,求解目标位姿最优解。结果利用Matlab软件平台,选择SVD,DQ以及文中算法进行比较,仿真实验结果表明,基于Hopfield神经网络和对偶四元数的位姿测量算法计算出的位姿参数误差最小。随着测量点数量的增大,文中提出的算法精度更高。结论对偶四元数同时求解位姿变换矩阵的旋转分量和平移分量,可消除计算误差,基于Hopfield神经网络和拉格朗日乘子法,可快速准确地计算,并收敛至目标位姿最优解。     

渗透系数反演的CHNN模型方法

利用连续型Hopfield神经网络(Continuous Hopfield Neural Network,简称CHNN)的反馈特性,结合实测资料和数值计算,构建了岩土体渗透系数的人工神经网络反演模型,通过网络神经元状态的变迁而最终稳定于平衡状态,从而得到渗透系数反演优化计算的结果。经实例验证,效果较好。     

Chaos and hyperchaos in a Hopfield neural network

In this paper we investigate numerically the parameter-space of an autonomous system of four nonlinear first-order ordinary differential equations, which represents a Hopfield neural network with four neurons. The study considers three independent two-dimensional cross-sections of the three-dimensional parameter-space generated by this mathematical model, every constructed considering Lyapunov exponent values. We show that is possible to completely characterize the dynamics of the system based in these three plots, which are representative of the three-dimensional parameter-space as a whole.     

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...