| 摘要: |
| 本文在分析概率逻辑神经元（ＰＬＮ）网络原型存在不足的基础上，提出ＰＬＮ元件的一个改进模型，并指出原来的ＰＬＮ网络模型、Ｈｏｐｆｉｅｌｄ学习律以及Ｂｏｌｔｚｍａｎ机的演化律等都是ＰＬＮ网络改进模型的特例．文中还给出ＰＬＮ网络改进模型在联想记忆应用中的模拟实验结果，说明改进模型无论在鲁棒性和收敛速度上比原型都有很大改进． |
| 关键词: 概率逻辑神经元（PLN）网络，A—学习律，广义A—学习律，联想记忆 |
| DOI： |
| 分类号: |
| 基金项目:国家攀登计划,863高技术研究与发展计划 |
|
| THE IMPROVEMENT OF PLN NETWORK AND ITS APPLICATIONS |
|
Zhang Bo,Zhang Ling
|
| Abstract: |
| In light of the deficiency of the original model of Probabilistic Logic Neuron (PLN)network, this paper presents a revised model and shows that its robustness and speed are improved. The authors also indicate that the original model of PLN network,the Hopfield's learning rule and the evolution rule of Boltzman machine are special cases of the new model. Finally, the application of the generalized PLN network to associative memory is given. The computer simulation results show that robustness and convergence speed of the new model are much better than the original one. |
| Key words: Probabilistic Logic Neuron (PLN) network A-learning rule generalized A -learning rule associative memory |
