Rao M S, Pujari A K
Department of Computer & Information Sciences University of Hyderabad, India.
Int J Neural Syst. 1999 Aug;9(4):351-70. doi: 10.1142/s0129065799000332.
A new paradigm of neural network architecture is proposed that works as associative memory along with capabilities of pruning and order-sensitive learning. The network has a composite structure wherein each node of the network is a Hopfield network by itself. The Hopfield network employs an order-sensitive learning technique and converges to user-specified stable states without having any spurious states. This is based on geometrical structure of the network and of the energy function. The network is so designed that it allows pruning in binary order as it progressively carries out associative memory retrieval. The capacity of the network is 2n, where n is the number of basic nodes in the network. The capabilities of the network are demonstrated by experimenting on three different application areas, namely a Library Database, a Protein Structure Database and Natural Language Understanding.
提出了一种新的神经网络架构范式，它作为联想记忆，同时具备剪枝和顺序敏感学习能力。该网络具有复合结构，其中网络的每个节点本身都是一个霍普菲尔德网络。霍普菲尔德网络采用顺序敏感学习技术，能够收敛到用户指定的稳定状态，且不存在任何虚假状态。这是基于网络和能量函数的几何结构。该网络的设计使得它在逐步进行联想记忆检索时允许按二进制顺序进行剪枝。网络的容量为2n，其中n是网络中基本节点的数量。通过在三个不同的应用领域进行实验，即图书馆数据库、蛋白质结构数据库和自然语言理解，展示了该网络的能力。
