洗牌型图样间联想光学神经网络模型

本文将洗牌型神经网络结构和图样间联想神经网络算法相结合,提出了一种洗牌型图样间联想神经网络(PS-IPA)模型。该模型具有极其简单、稀疏的互连权矩阵,十分适于大规模神经网络的光学实现。计算机模拟结果表明洗牌型图样间联想神经网络的稳定性和抑制噪音的能力均优于图样间联想网络IPA.本文还给出了洗牌互连的一般性原则,使网络结构得到优化,增强了洗牌型神经网络的灵活性和适应性。并采用3-洗牌和2-洗牌结合的PS-IPA对汽车牌照的字符进行识别,得到了较好的结果。

A PERFECT SHUFFLE TYPE OF INTERPATTERN ASSOCIATION OPTICAL NEURAL NETWORK MODEL

A perfect shuffle type of interpattern association(PS-IPA) neural network model is developed by the combination of IPA with perfect shuffle (PS) interconnected architecture.A highly sparse interconnection weight matrix (IWM) with only three gray levels can be obtained from the new model,and makes it easier to realize a large-scale optical neural system.The results of computer simulations and the optical character recognition (OCR) by PS-IPA have shown improved performances compared with the IPA model.A generalized α-shuffle principle is also given, which enhances the flexibility of the perfect shuffle type of neural networks (PSNN).The vehicle license numbers in 27×16 array were recognized by our PS-IPA neural system with a hybrid 2-shuffle and 3-shuffle strategy,and good recognizing results were gained.