Hopfield 网络模型 相互连接型的神经网络模型,简称为 HNN (Hopfield Neural Network),解决了具有 NPC 复杂性的旅行商问题(TSP) 对比: MP模型、感知器模型、自适应神经元Adaline、EBP网络:属于前向神经网络。 学习观点:是强有力的学习系统,结构简单,易于编程 系统观点:静态的非线性映射,通过简单非线性处理单元复合映...
霍普菲尔德神经网络(Hopfield neural network)是一种循环神经网络,由约翰·霍普菲尔德在1982年发明。Hopfield网络是一种结合存储系统和二元系统的神经网络。它保证了向局部极小的收敛,但收敛到错误的局部极小值(local minimum),而非全局极小(global minimum)的情况也可能发生。霍普菲尔德网络也提供了模拟人类记忆的模型 Hopf...
class Hopfield: def __init__(self, num_in): self.num_in=num_in self.wight=makematrix(num_in, num_in) def determine_wight(self,inputs): for x in inputs: self.wight+=np.dot(x.reshape(-1,1),x.reshape(1,-1)) return self.wight-self.wight[2][2]*numpy.matlib.identity(self.num...
Hopfield最早提出的网络是二值神经网络,各神经元的激励函数为阶跃函数或双极值函数,神经元的输入、输出只取{0,1}或者{ -1,1},所以也称为离散型Hopfield神经网络DHNN(Discrete Hopfiled Neural Network)。在DHNN中,所采用的神经元是二值神经元;因此,所输出的离散值1和0或者1和-1分别表示神经元处于激活状态和抑制...
The training algorithm of the Hopfield neural network is simple and is outlined below: 1. Learning: Assign weights wij to the synaptic connections: (7.68)wij=∑k=1Keikejk,ifi≠j0,ifi=jKeep in mind that wij=wji, so it is necessary to perform the preceding computation only for i<j. 2....
1、连续型Hopfield神经网络 Continuous Hopfield Neural NetworkCHNN 连续型Hopfield神经网络是Hopfield于1984年在离散型Hopfield神经网络的基础上提出的。Hopfield网络分为离散型和连续型两种,都是对称互连网络(Wij=Wji),根据节点状态的取值来划分是离散型的还是连续型。离散网络节点取-1,+1或0,+1,连续网络节点状态在某...
J., David I. Feinstein, and Richard G. Palmer. "" Unlearning" has a stabilizing effect in ...
Together with back propagation, Hopfield network was one of the main reasons for the resurgence of interest in neural networks in the 1980s Hopfield Networks are beautifully simple devices that can be used for storing memories as distributed patterns of activity。
Hopfield网络是单层对称全反馈网络,根据其激活函数的选取不同,可分为离散型Hopfield网络(Discrete Hopfield Neural Network,简称 DHNN)和连续型 Hopfield 网络(Continue Hopfield Neural Network,简称CHNN)。离散型Hopfield网络的激活函数为二值型阶跃函数,主要用于联想记忆、模式分类、模式识别。这个软件为离散型Hopfield网络...
Whereas, the traditional continuous Hopfield neural network (Continuous Hopfield Neural Network, CHNN) has the defects of poor penalty parameter robustness and the resulting solution that easy to be trapped in local optimality when solving such problems, based on the Metropolis thought in the simulated...