首页 | 本学科首页   官方微博 | 高级检索  
     


Learning Algorithm and Retrieval Process for the Multiple Classes Random Neural Network Model
Authors:Aguilar  Jose
Affiliation:(1) CEMISID. Dpto. de Computación, Facultad de Ingeniería, Universidad de los Andes, Av. Tulio Febres, Mérida, Venezuela
Abstract:Gelenbe has modeled neural networks using an analogy with queuing theory. This model (called Random Neural Network) calculates the probability of activation of the neurons in the network. Recently, Fourneau and Gelenbe have proposed an extension of this model, called multiple classes random neural network model. The purpose of this paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon non-linear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. In addition, we propose a progressive retrieval process with adaptive threshold values. The experimental evaluation shows that the learning algorithm provides good results.
Keywords:color pattern recognition  learning algorithm  multiple classes random neural network  retrieval process
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号