多时滞Hopfield神经网络的鲁棒稳定性及吸引域的估计

通过构造适当的Lyapunov泛函及扇区条件,对一类具有参数摄动的多时滞Hopfield神经网络模型的鲁棒稳定性进行了分析,给出了平衡点渐近稳定的充分条件.利用矩阵范数的定义及性质,得到了一个以范数形式表示的推论,并提供了一种估计网络渐近稳定平衡点吸引域的方法.仿真结果进一步证明了结论的有效性.     

Associative Memories in Infinite Dimensional Spaces

A generalization of the Little–Hopfield neural network model for associative memories is presented that considers the case of a continuum of processing units. The state space corresponds to an infinite dimensional euclidean space. A dynamics is proposed that minimizes an energy functional that is a natural extension of the discrete case. The case in which the synaptic weight operator is defined through the autocorrelation rule (Hebb rule) with orthogonal memories is analyzed. We also consider the case of memories that are not orthogonal. Finally, we discuss the generalization of the non deterministic, finite temperature dynamics.     

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.     

基于离散Hopfield神经网络的噪声数字识别

在Hebb学习规则的基础上,运用离散Hopficld神经网络的联想记忆能力,对含有噪声而产生畸变的0~9数字进行了识别。通过改进神经网络的记忆样本,即先对记忆样本做正交化处理,再对改进后的记忆样本进行学习,得到相应的权值矩阵,然后利用改进后的离散Hopfield神经网络根据待识别噪声数字的信息联想已记忆的数字。实验结果表明,改进后的神经网络对噪声数字有较好的识别效果,提高了记忆能力和识别的正确率。     

一种基于离散Hopfield神经网络的RTOS功耗优化方法

RTOS(Real-Time Operating System,实时操作系统)是SoC(System-on-a-Chip,系统芯片或片上系统)的一个重要组成部分,其功耗一般约占整个系统功耗30～40%的比例,而基于软/硬件划分的RTOS功耗优化方法(简称RTOS-Power划分)能够明显地减少SoC的功耗.因此,文中首先引入了RTOS-Power划分问题的一个新模型,这有助于理解RTOS-Power划分的本质.然后,提出了一种基于离散Hopfield神经网络的RTOS-Power划分方法,重新定义了神经网络的神经元表示、能量函数、运行方程和系数.最后,对该方法进行了仿真实验,并同遗传算法和蚂蚁算法进行了性能比较.实验结果表明:该文提出的方法能够以相对较小的代价(FPGA开销小于4K个可编程逻辑块)取得高达60%的功耗节省,同时,与纯软件实现的RTOS相比,系统性能也得到了相应的提高.     

Portfolio selection using neural networks

In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the NN heuristic and we compare them to those obtained with three previous heuristic methods. The portfolio selection problem is an instance from the family of quadratic programming problems when the standard Markowitz mean-variance model is considered. But if this model is generalized to include cardinality and bounding constraints, then the portfolio selection problem becomes a mixed quadratic and integer programming problem. When considering the latter model, there is not any exact algorithm able to solve the portfolio selection problem in an efficient way. The use of heuristic algorithms in this case is imperative. In the past some heuristic methods based mainly on evolutionary algorithms, tabu search and simulated annealing have been developed. The purpose of this paper is to consider a particular neural network (NN) model, the Hopfield network, which has been used to solve some other optimisation problems and apply it here to the portfolio selection problem, comparing the new results to those obtained with previous heuristic algorithms.     

一种基于免疫和Hopfield神经网络的多峰值优化算法

分析了免疫算法和Hopfield神经网络的优缺点,提出了一种解决多峰值函数优化问题的混合算法。Hopfield神经网络易于硬件实现,具有简单、快速的优点,但是对初始值具有依赖性以及容易陷入局部极值。免疫算法具有识别多样性的特点,但搜索效率和精度不高。将两算法结合起来,优势互补。首先用免疫算法寻优,然后对所得具有全局多样性的解进行聚类分析,所得聚类中心作为Hopfield神经网络的初始搜索点,最后利用Hopfield神经网络逐个寻优。实验表明,该算法是一种有效的求解多峰函数优化问题的方法,与免疫算法相比,搜索效率和精度都较高。     

五种人民币序列号识别算法抗噪能力比较

为了比较不同的人工神经网络算法识别人民币序列号的性能,研究了离散Hopfield神经网络、BP神经网络、PNN神经网络、GRNN神经网络、SVM神经网络等五种算法的训练耗时、识别速度、识别率和抗噪声能力. 研究结果表明,在五种算法中BP算法的综合表现最差,其次为SVM和Hopfield算法,而PNN和GRNN算法表现最好,不仅识别率最高、训练和识别时间最短,而且具有较强的抗噪声能力.     

基于离散Hopfield网络的自相关过程控制方法

对于自相关过程的统计控制,传统统计学方法虚发警报的概率较大,而BP人工神经网络方法权值训练困难,灵敏度不高。提出一种基于联想学习与离散Hopfield网络的自相关过程控制方法。不需任何训练样本,通过正交化编码将过程状态以吸引子的形式存储到Hopfield网络中,并利用网络的联想功能来检测自相关过程的阶跃型突变。算例研究表明,与Elman网络和EWMA方法相比,过程正常时,所提方法的平均链长（ARL）分别提高了27.9%和55.0%;过程异常时,所提方法的ARL分别降低了74.1%与81.8%以上。说明了方法的有效性与优越性。     

基于免疫进化规划的多用户检测技术研究

把人工免疫系统和神经网络系统的信息处理机制引入到进化规划算法（EP），提出了免疫进化规划算法．所提IEP通过使用随机Hopfield神经网络制备疫苗构成新的免疫算子，把新的免疫算子结合到进化规划中，不仅加快了进化规划的收敛速度，并提高了进化规划的全局收敛能力．然后在CDMA系统利用此算法设计了新的多用户检测器．仿真结果证明了该方法能够快速收敛到全局最优解，并且无论抗多址干扰和抗远近效应能力都优于传统方法和一些应用优化算法的多用户检测器．