Agriculture satellite image segmentation using a modified artificial Hopfield neural network

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region’s relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images.     

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.     

Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

复数Hopfield神经网络盲均衡QAM信号

为了解决复数多值信号的盲均衡问题,本文提出了基于复数Hopfield神经网络盲均衡多值信号的方法:将基于Hopfield神经网络的盲均衡算法从实数域推广到复数域.在复数域成功构造了复数Hopfield神经网络,重点针对16QAM信号进行盲均衡.并验证了此系统可以处理非统计量字符,即处理16QAM信号的Hopfield神...     

反馈电压偏置型Hopfield 网络电域盲检测相干光通信信号

为解决无线分集相干光接收机的自适应盲检测问题,提出了一种新的离散时间连续状态的网络输出反馈偏置型的复Hopfield 神经网络用以解决多值QAM 信号的盲检测问题。反馈电压偏置的引入即不脱离传统Hopfield 模型,又能有效满足多值信号检测时所需的搜索空间变大的特殊要求。全文完成多值信号盲检测的优化问题构造和能量函数的映射,给出能量函数的证明、分析和它的约束条件,给出适用该问题的激活函数的基本特征,正确盲检测信号的权矩阵的配置方法。最后,通过详细的仿真结果展示和与其他算法性能对比进一步验证算法的有效性和优越性并指出算法所存在的问题和下一步的研究方向。     

Stability region bifurcations of nonlinear autonomous dynamical systems: Type‐zero saddle‐node bifurcations

The behavior of stability regions of nonlinear autonomous dynamical systems subjected to parameter variation is studied in this paper. In particular, the behavior of stability regions and stability boundaries when the system undergoes a type‐zero sadle‐node bifurcation on the stability boundary is investigated in this paper. It is shown that the stability regions suffer drastic changes with parameter variation if type‐zero saddle‐node bifurcations occur on the stability boundary. A complete characterization of these changes in the neighborhood of a type‐zero saddle‐node bifurcation value is presented in this paper. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

复值Hopfield神经网络盲恢复8PSK信号

传统的神经网络恢复MPSK信号时,因接收信号是复值的,而采用将信号实部与虚部分开盲恢复,该类方法仍存在无法解决的多值信号盲恢复问题.本文利用接收数据正交补投影和发送信号属于有限字符集的先验知识基础上,在异步调整模式下,设计出复值空间的激励函数,将其引入神经网络的能量函数中,推出了一种基于离散复值Hopfield神经网络...     

On asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks

In this paper, we obtain some sufficient conditions for determining the asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks by utilizing the Lyapunov functional method. An example is given to show the validity of the results.     

复数离散Hopfield网络盲检测64QAM信号

针对复数多电平QAM信号的盲检测问题,该文提出了一个新的复数离散多电平Hopfield神经网络。该网络的实部、虚部各含一个多电平离散激励实函数。该文分析了经典两电平离散Hopfield神经网络能量函数的局限性,构造了一个新的复数多电平神经网的能量函数,并用此能量函数讨论了神经网的稳定性。当该神经网的权矩阵借助接收数据补投影算子构成时,该复数离散多电平Hopfield网络可有效地求解带整数约束的二次规划问题,从而实现QAM信号盲检测。仿真试验表明:该算法所需接收数据较短,就可到达全局真平衡点,计算难度大大降低,具有良好的快速性。