基于混沌粒子群算法和Hopfield网络求解TSP问题

针对Hopfield网络求解TSP问题经常出现局部最优解,该文将混沌粒子群算法(PSO)与之结合,提出一种基于混沌粒子群的Hopfield神经网络方法。通过实验将其与文献[5,8]以及"PSO+HNN"策略比较,验证了该文算法不仅能够以更大概率收敛到全局最优,而且耗时更少。     

基于混沌粒子群算法和Hopfield网络求解TSP问题

针对Hopfield网络求解TSP问题经常出现局部最优解,该文将混沌粒子群算法（PSO）与之结合,提出一种基于混沌粒子群的Hopfield神经网络方法。通过实验将其与文献[5,8]以及“PSO＋HNN”策略比较,验证了该文算法不仅能够以更大概率收敛到全局最优,而且耗时更少。     

基于神经网络量子算法的多用户检测器

利用遗传量子算法和Hopfield神经网络，提出了一种融合两种算法优点的神经网络量子算法，并将其应用到CDMA通信系统的多用户检测问题中。所提算法把神经网络嵌入到遗传量子算法的每一代中，可进一步提高量子种群的适应度函数值。通过混合神经网络到GQA中，还可加快GQA的收敛速度进而减少算法的计算复杂度。另外，GQA所提供的良好初值改善了HNN的性能，嵌入的HNN也提高了GQA的性能。仿真结果证明了该方法的抗多址干扰能力和抗远近效应能力都优于传统检测器和一些应用智能算法的多用户检测器。     

一类求解最大独立集问题的混合神经演化算法

提出一类求解最大独立集问题(MIS)的混合型神经演化算法．该算法基于空间剖分与“排除”策略，有效综合了神经网络快速收敛及遗传算法稳健全局搜索的特别优点．与标准遗传算法和神经网络算法相比，该算法显示了极高的全局优化性态与计算效率．     

基于CS的Hopfield神经网络数字识别应用

介绍了布谷鸟搜索(cuckoo search, CS)和Hopfield神经网络的基本原理,研究了基于Hopfield神经网络的数字识别应用。针对Hopfield网络权值在数字识别时易陷入局部最优,提出将CS引入Hopfield神经网络的解决方法。利用CS对复杂、多峰、非线性极不可微函数的全局搜索能力,使Hopfield网络在较高噪信比的情况下仍保持较高的联想成功率,并进行了仿真。仿真结果表明,该方法识别数字的效果更佳。     

Fusion of artificial neural networks for learning capability enhancement: Application to medical image classification

Artificial neural network (ANN) is one of the commonly used tools for computational applications. The specific advantages of ANN are high accuracy, less convergence time, less computational complexity, and so forth. However, all these merits are not available in the same ANN. Even though back propagation neural (BPN) networks are accurate, their computational complexity is significantly high. BPN networks are also not stable. On the other hand, Hopfield neural network (HNN) is better than BPN in terms of computational efficiency. But the accuracy of HNN is low. In this work, a modified ANN is proposed to overcome this specific problem. The modified ANN is a fusion of BPN and HNN. The technical concepts of BPN and HNN are mixed in the training algorithm of the proposed back propagation‐Hopfield network (BPHN). The objective of this fusion is to improve the performance of conventional ANN. Magnetic resonance brain image classification experiments are used to analyse the proposed BPHN. Experimental results have suggested improvement in the learning process of the proposed BPHN. A comparative analysis with the conventional networks is performed to validate the performance of the proposed approach.     

Combining tabu Hopfield network and estimation of distribution for unconstrained binary quadratic programming problem

Unconstrained binary quadratic programming problem (UBQP) consists in maximizing a quadratic 0–1 function. It is a well known NP-hard problem and is considered as a unified model for a variety of combinatorial optimization problems. This paper combines a tabu Hopfield neural network (HNN) (THNN) with estimation of distribution algorithm (EDA), and thus a THNN–EDA is proposed for the UBQP. In the THNN, the tabu rule, instead of the original updating rule of the HNN, is used to govern the state transition or updating of neurons to search for the global minimum of the energy function. A probability vector in EDA model is built to characterize the distribution of promising solutions in the search space, and then the THNN is guided by the global search information in EDA model to search better solution in the promising region. Thus, the short term memory of the tabu mechanism in the THNN cooperates with the long term memory mechanism in the EDA to help the network escape from local minima. The THNN–EDA is tested on 21 UBQP benchmark problems with the size ranging from 3000 to 7000, and 48 maximum cut benchmark problems, a special case of the UBQP, with the size ranging from 512 to 3375. Simulation results show that the THNN–EDA is better than the other HNN based algorithms, and is better than or competitive with metaheuristic algorithms and state-of-the-art algorithms.     

TSP及其基于Hopfield网络优化的研究

Ｈｏｐｆｉｅｌｄ网络（ＨＮＮ）是一种有效的优化模型,但存在易收敛到非法解或局部极小以及对模型参数与初值依赖性强的缺点。旅行商问题（ＴＳＰ）是研究算法性能的典型算例,通过对其进行计算机仿真优化,分析归纳了ＨＮＮ模型存在缺点的原因,总结并提出若干改进方法与思想。同时,针对ＴＳＰ问题的工程背景提出了若干发展性研究内容与方法。     

Competitive Hopfield Network Combined With Estimation of Distribution for Maximum Diversity Problems

This paper presents a discrete competitive Hopfield neural network (HNN) (DCHNN) based on the estimation of distribution algorithm (EDA) for the maximum diversity problem. In order to overcome the local minimum problem of DCHNN, the idea of EDA is combined with DCHNN. Once the network is trapped in local minima, the perturbation based on EDA can generate a new starting point for DCHNN for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. The proposed algorithm is tested on 120 benchmark problems with the size ranging from 100 to 5000. Simulation results show that the proposed algorithm is better than the other improved DCHNN such as multistart DCHNN and DCHNN with random flips and is better than or competitive with metaheuristic algorithms such as tabu-search-based algorithms and greedy randomized adaptive search procedure algorithms.      

Chaotic simulated annealing with decaying chaotic noise

By adding chaotic noise to each neuron of the discrete-time continuous-output Hopfield neural network (HNN) and gradually reducing the noise, a chaotic neural network is proposed so that it is initially chaotic but eventually convergent, and, thus, has richer and more flexible dynamics compared to the HNN. The proposed network is applied to the traveling salesman problem (TSP) and that results are highly satisfactory. That is, the transient chaos enables the network to escape from local energy minima and to find global minima in 100% of the simulations for four-city and ten-city TSPs, as well as near-optimal solutions in most of runs for a 48-city TSP.     

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。     

基于Hopfield神经网络的FLIR图像分割

针对前视红外(FLIR)图像的分割,在基于模型的FLIR图像分割算法所提出的全 局准则函数及初始概率确定方法的基础上.建立了与之相对应的Hopfield网络的能量函数 及网络的初始状态,当网络运行达到稳定状态后,便可获得图像的分割结果.分析了能量函数 中,目标函数与约束条件的加权系数对分割结果的影响,并根据分割结果的非模糊性准则,提 出了一个确定加权系数的、简单有效的方法.给出了针对真实红外目标图像的分割结果.     

基于改进Hopfield神经网络的图像特征点匹配算法

针对图像特征点匹配算法的运行时间呈指数增长的问题,提出了一种新的匹配算法NHop.该算法通过加入新的网络输入输出函数、点对间差异的度量和启发式选择目标点的方式,对传统的Hopfield神经网络进行了改进.新算法不仅解决了传统Hopfield神经网络运行时间长、能量函数易陷入局部极小点的问题,而且也有效地实现了图像特征点的匹配.实验结果表明,与传统的Hopfield神经网络相比,NHop算法的匹配速度更快、准确率更高,对于图像特征点的匹配效果更好.     

一种混沌Hopfiele网络及其在优化计算中的应用

文章讨论了神经网络算法在约束优化问题中的应用,提出了一种混沌神经网络模型。在Hopfield网络中引入混沌机制,首先在混沌动态下搜索,然后利用HNN梯度优化搜索。对非线性函数的优化问题仿真表明算法具有很强的克服陷入局部极小能力。     

一种混沌Hopfield网络及其在优化计算中的应用

文章讨论了神经网络算法在约束优化问题中的应用,提出了一种混沌神经网络模型。在Hopfield网络中引入混沌机制,首先在混沌动态下搜索,然后利用HNN梯度优化搜索。对非线性函数的优化问题仿真表明算法具有很强的克服陷入局部极小能力。     

TSP问题的瞬态混沌神经网络算法

采用具有瞬态混沌特性的神经网络(TCNN)解TSP问题。利用神经元的自抑制反馈产生混沌动态，其遍历性能和随机搜索性能有效地克服了Hopfield神经网络(HNN)极易陷入局部极小的缺陷，同时利用一时变参数控制混沌行为，使网络再经过一个短暂的倍周期倒分岔后逐渐趋于一般的Hopfield神经网络，从而收敛到一个最优或近似最优的稳定平衡点。仿真结果表明，TCNN比HNN具有更强的全局寻优能力和更高的搜索效率。     

物流中心选址算法改进及其Hopfield神经网络设计

在分析物流中心选址传统算法的基础上,引入一种新的选址模型,该模型能减少决策变量和约束条件的个数.利用该模型设计了一种Hopfield神经网络,将约束合并进网络结构从而将罚函数从能量函数中消除,使得网络的运行时间显著降低.为物流中心选址优化提供了一种新的方法.     

基于遗传算法优化神经网络的多用户检测

利用遗传算法全局搜索能力强和反向传播(BP)算法局部搜索速度快的特点,采取两段式训练方法,既避免陷入局部最小,又加快收敛速度。提出基于遗传算法优化神经网络权值的多用户检测算法。采用实数编码方式,将传统神经网络的能量函数作为适应度函数,选择算子选用轮盘赌算子,交叉算子选用单点交叉算子,变异算子选用正态变异算子。仿真结果表明,该算法的误码率、信干比和信道跟踪能力等方面的性能与传统前馈神经网络多用户检测算法相比均有一定的改善。     

Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks

Due to mobility of wireless hosts, routing in mobile ad-hoc networks (MANETs) is a challenging task. Multipath routing is employed to provide reliable communication, load balancing, and improving quality of service of MANETs. Multiple paths are selected to be node-disjoint or link-disjoint to improve transmission reliability. However, selecting an optimal disjoint multipath set is an NP-complete problem. Neural networks are powerful tools for a wide variety of combinatorial optimization problems. In this study, a transient chaotic neural network (TCNN) is presented as multipath routing algorithm in MANETs. Each node in the network can be equipped with a neural network, and all the network nodes can be trained and used to obtain optimal or sub-optimal high reliable disjoint paths. This algorithm can find both node-disjoint and link-disjoint paths with no extra overhead. The simulation results show that the proposed method can find the high reliable disjoint path set in MANETs. In this paper, the performance of the proposed algorithm is compared to the shortest path algorithm, disjoint path set selection protocol algorithm, and Hopfield neural network (HNN)-based model. Experimental results show that the disjoint path set reliability of the proposed algorithm is up to 4.5 times more than the shortest path reliability. Also, the proposed algorithm has better performance in both reliability and the number of paths and shows up to 56% improvement in path set reliability and up to 20% improvement in the number of paths in the path set. The proposed TCNN-based algorithm also selects more reliable paths as compared to HNN-based algorithm in less number of iterations.     

一种基于退火策略的混沌神经网络优化算法

Ｈｏｐｆｉｅｌｄ网络（ＨＮＮ）中引入混沌机制,首先在混沌动态下粗搜索,并利用退火策略控制混沌动态退出和逆分贫出现,进而ＨＮＮ梯度优化搜索,提出了一种具有随机性和确定性并存的优化算法,对经典旅行商（ＴＳＰ）的研究,表明算法具有很强的克服陷入局部极小能力,较大程度提高了优化、时间和对初值的鲁棒性能,同时给出了模型参数对性能影响的一些结论。