一种改进型离散Hopfield学习算法

针对离散Hopfield 神经网络(DHNN) 的权值设计问题, 提出一种改进型学习算法, 并在DHNN动力学分析的基础上设计该学习算法. 利用矩阵分解的方法(MD) 得到正交矩阵, 并采用得到的正交矩阵直接计算DHNN的权值矩阵. 通过该学习算法得到的权值矩阵, 可以很好地存储训练样本的信息, 使测试样本收敛到稳定点. 该学习算法不需要进行分块计算, 减少了计算步骤和计算量, 降低了网络的迭代次数, 从而提高了网络运行速度. 最后, 将该学习算法应用于水质评价, 验证了其有效性和可行性.

An improved learning algorithm for discrete Hopfield

According to the weight design problem of discrete Hopfield neural network(DHNN), an improved learning algorithm for weight design is proposed. On the basis of the dynamic analysis for DHNN, the learning algorithm is designed. The orthogonal matrix is got by using the method of matrix decomposition(MD), and the orthogonal matrix is used to get the weight matrix of DHNN directly. The weight matrix which is obtained by the learning algorithm can store information well, so that the testing sample can converge to a stable point. The learning algorithm does not need block calculation. The calculation steps, the amount of calculation and the number of iterations are reduced, so the operating speed of the network is improved. Finally, the algorithm is applied to water quality evaluation to prove its effectiveness and feasibility.