hopfield neural network中文是什么意思

• 霍普菲尔德神经网络

• neural network    【计算机】模拟脑神经元网络〔一种模拟人脑神经细胞的电脑系 ...
• hopfield network    霍普菲尔网络
• neural network    【计算机】模拟脑神经元网络〔一种模拟人脑神经细胞的电脑系统〕。
• alternating projection neural network    交替投影神经网络
• ann artificial neural network    人工神经网络
• articificial neural network    类神经网络
• artificial neural network    类神经网路; 人工神经网络
• artificial neural network (ann)    类神经网络
• artificial neural network system    人工神经元网络系统
• biological neural network    神经网络
• chaotic neural network    混沌神经网络
• cnn cellular neural network    蜂窝神经网络
• cnn computational neural network    计算的神经中枢网
• connectionist neural network    连接机制神经网络
• european neural network society    欧洲神经网络学会; 欧洲神经元网络
• feed-forward neural network    反向传输神经网络
• fnn fuzzy neural network    模糊神经网络
• fuzzy neural network    模糊神经网络
• fuzzy-neural network    模糊神经网络
• higher order neural network    高阶神经网络
• hnn hybrid neural network    混合神经网络
• ieee neural network council    ieee神经网络委员会
• japanese neural network society    日本神经网络协会
• knowledge and neural network    知识和神经网络
• mp neural network    mp神经网络

• Chapter 2 different to the stability of the 1 chapter , we studied the stability of ? gain stability . we have known that though the system have lyapnov stability it may be not have ? gain stability . it is necessary to discuss the ? gain stability . chapter 3 we discuss the exponential stability of a new hopfield neural network : bam neural network
  第三章，利用dini导数、不等式以及lyapunov函数方法等工具对当今研究的一类热点神经网络， （ bam ）双向关联的神经网络的指数渐近稳定性作了研究，得到了其指数渐近稳定的条件
• The similarity between the energy function of discrete hopfield neural network ( dhnn ) and the formula of omd means that using dhnn to solve the problem of omd is feasible . the convergence process of neural network is the process to solve the problem of omd
  离散hopfield网络（ dhnn ）能量函数和最优多用户检测求解公式之间的相似性，从理论上表明可以利用离散hopfield网络来进行最优多用户检测，神经网络收敛的过程就是求解的过程，从而得到一种基于神经网络的多用户检测器。
• Simulation results indicate that the drnn identifier is more accurate than the traditional ked theoretical model . ( 5 ) with reference to the parameter estimation problem , a linearized model of the experimental mechanism is established , whose state equation is determined by means of the continuous hopfield neural networks ( hnn )
  5 .在参数估计方面,本文构造了弹性连杆机构动力学系统的线性化模型,根据连续型hopfield神经网络( hnn )的优化计算原理,对该线性化模型的状态方程进行了参数估计。
• Hopfield neural network is a full - linked interconnected dynamical system with stronger computational ability than feedforward neural network , also it can be easy implemented by vlsi and it ' s solution can be obtained in millisecond , it provide a powerful tool for on - line observer design
  Hopfield神经网络是一种全连接、互反馈的动力学系统，比前向神经网络具有更强的计算能力，由于它可以通过vlsi等硬件物理实现，而且问题的求解可以在毫秒级完成，因此为观测器的在线适时设计提供了有力的工具。
• Chapter 4 discusses how to stabilize a class of time - delays neural networks via standard feedback control ; this kind of neural networks provides a unified view of several well - known neural networks ( such as hopfield neural networks and cellular neural networks ) with discrete delays or distributed delays . a stability criterion is given by using the lyapunov method . all the results obtained in this chapter are stated in simple algebraic forms so that their verifications and applications are straightforward and convenient
  通过使用lyapunov方法,我们得到了一类时延神经网络经过标准反馈控制后的稳定条件,这类神经网络涵盖了几种著名的神经网络,如hopfield神经网络、细胞神经网络,同样,本章所得到的结论也是简单的代数表达式,使用非常方便,最后在用计算机通过做数值实验对这些结论进行了验证。
• Our results are applied to classical hopfield neural networks with distributed delays and some novel asymptotic stability criteria are also derived 4 ) estimation of exponential convergence rate and exponential stability for neural networks with time - varying delay some new criteria for exponential stability are derived and establish an estimation of the exponential convergence rate by constructing an appropriate lyapunov functional and using the linear matrix inequality ( lmi ) approach . the derived results are applicable to
  我们的结果适用于典型的带分布式时滞的hopfield神经网络,并且得出了一些新颖的渐近稳定性判别准则4 )变时滞的神经网络的指数收敛速度的估计和指数稳定性分析通过构造适当的lyapunov - krasovskii泛函,利用线性矩阵不等式( lmi )技术,得到了新的指数稳定的充分条件,并建立一个估计指数收敛速度的方法。
• By combining chaotic dynamics and converging dynamics together , the neural network transit gradually to hopfield neural network is made . by introducing converging factor , the aim of controlling chaos is attained , which provides initial value of hopfield neural network that is near to the global optimal solutions , and solve the problem of local minimum . the principle of genetic algorithm is analyzed , and the design and of genetic algorithm are studied
  通过把混沌动力学与收敛动力学相结合，使网络逐渐由混沌神经网络向hopfield网络过渡，达到控制混沌的目的，并且提供了一个在全局最优解附近的初值，避开了神经网络权值初始化没有理论依据的难题，无须确定连接权值和阈值，使神经网络具有物理意义明确、便于与工程应用相结合的特点。
• In order to apply hopfield neural network to optimization problem , we should translate object function into energy function and make the variables of the problem correspond to the states of the network . when the energy function converges at minimal value , the states of the network correspond to the optimal solution of the problem
  将hopfield网络应用于求解优化问题，就是把目标函数转化为网络的能量函数，把问题的变量对应于网络的状态，当网络的能量函数收敛于极小值时，网络的状态就对应于问题的最优解。
• Our condition and estimate are formulated in terms of the network parameters , the neurons ’ activation functions and the associated equilibrium point . hence , they are easily checkable . it is believed that these results are significant and useful for the design and applications of the delayed hopfield neural networks
  这些条件和估计的公式是由网络参数、神经元激活函数以及相应的平衡点构成,所以它们很容易使用,相信这些结果对于带时间延迟的hopfield神经网络的设计和应用具有一定的重要性和使用价值。
• The image reconstruction algorithms is thoroughly researched . the filter back projection ( fbp ) , algebra reconstruction technology ( art ) and fan beam data rearrangement algorithm used in medical computer tomography are improved . the noser algorithm , linear neural networks method and hopfield neural networks method are presented and gain the better result of image reconstruction
  深入研究了图像重建算法，改进了医学ct的滤波反投影算法、代数重建技术和扇束投影数据重排方法，提出了带图像光滑约束的noser算法、线性神经网络方法和hopfield神经网络方法，并得到了较好的图像重建结果。