A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem

This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH–EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.     

Modelling microtubules in the brain as n-qudit quantum Hopfield network and beyond

The scientific approach to understand the nature of consciousness revolves around the study of the human brain. Neurobiological studies that compare the nervous system of different species have accorded the highest place to humans on account of various factors that include a highly developed cortical area comprising of approximately 100 billion neurons, that are intrinsically connected to form a highly complex network. Quantum theories of consciousness are based on mathematical abstraction and the Penrose–Hameroff Orch-OR theory is one of the most promising ones. Inspired by the Penrose–Hameroff Orch-OR theory, Behrman et al. have simulated a quantum Hopfield neural network with the structure of a microtubule. They have used an extremely simplified model of the tubulin dimers with each dimer represented simply as a qubit, a single quantum two-state system. The extension of this model to n-dimensional quantum states or n-qudits presented in this work holds considerable promise for even higher mathematical abstraction in modelling consciousness systems.     

An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization.     

基于Hopfield神经网络的盲检测数字水印

提出了一种基于Hopfield神经网络的盲检测数字水印算法。基于噪声可见函数实现了水印的自适应嵌入,利用Hopfield神经网络记忆宿主图像以及原始水印信息。在水印检测时,通过神经网络从嵌入水印的图像中联想出宿主图像和水印嵌入信息,再利用嵌水印图像和联想出的宿主图像提取出水印,实现了水印的盲检测。     

基于WEB嵌入式自适应智能控制系统的研究与开发

提出一种基于WEB嵌入式自适应智能控制系统设计方案,提供了各组成模块软硬件的详细设计与具体实现;系统采用主流的S3C2410作为处理器,通过I2C总线实现各硬部件的连接;开发的嵌入式WEB服务器模块,使得用户能够对温度传感器读数进行互联网远程监测和多路数字、模拟量输出智能控制;并且神经网络模块能够自适应地学习和控制温度读数与制冷设备输出数之间的关系。实验结果显示改进后的Hopfield算法的收敛性和记忆性得到了提高,并使该系统具有较大的应用价值。     

自组织实时分布式故障诊断系统

以往的分布式故障诊断系统只有当故障发生后才进行故障诊断,不具有实时性,不能监测网络的运行并根据网络的运行情况适时地启动故障诊断模块。给出了一种新的分布式故障诊断系统,既克服了网络频繁进行故障诊断代价太高的缺点又使网络的故障得到及时发现。     

运输问题的神经网络解法

给出了利用Hopfield连续模型求解运输问题的数值算法,是对运筹学知识的补充和完善,是借助人工神经网络计算机原理解决组合优化问题的一个成功范例。     

人工神经网络在图论中的应用

人工神经剐络与图论之闻有密切的联系。本文把人工神经网络应用于图论的问题求解中,利用Hopfield网络对图的着色、图的最大独立集和最大团进行求解,构造了各自的能量函数,进而得出网络的运行方程。     

A hybrid HNN-QP approach for dynamic economic dispatch problem

This paper introduces a solution of the dynamic economic dispatch (DED) problem using a hybrid approach of Hopfield neural network (HNN) and quadratic programming (QP). The hybrid algorithm is based on using enhanced HNN; to solve the static part of the problem; the QP algorithm for solving the dynamic part of the DED. This technique guarantees the global optimality of the solution due to its look-ahead capability. The new algorithm is applied and tested to an example from the literature and the solution is then compared with that obtained by some other techniques to prove the superiority and effectiveness of the proposed algorithm.     

神经网络原理及其能量函数的构造

本文简要论述神经网络概念、原理及发展史,探讨神经网络中有关能量函数的构造方法和技巧。