摘要
符号推理系统已成功地得到应用,但仍有一些问题仅仅使用纯符号处理技术是难以解决的,如人的视觉功能等。这类领域中,基于人工神经网络的系统大有希望。然而,由于目前的神经网络学习效率低、训练时间长等问题,使其实用化大受影响。所以本文提出了一个混合型系统,即符号一神经网络系统。这个系统充分利用符号和神经网络系统的各自优点,不仅具有好的逻辑思维能力,而且具有好的形象思维能力。
Symbolic inference systems have been successfully used in the process control and me-
dical diagnosis.But there are some problems that are hard to solve with pure symbolic processing te-
chnology,such as vision function.Neural network-based systems are now used in these fields and show
that there are hopes to solve these problems.Because the neural network learning efficiency is low
and the time of training network is long in some cases,neural networks have not gained increasingly
wide application.In this paper,the hybrid system,i.e.symbolic-neural network system,is presented.
This system makes good use of the advantages of symbolic and neural network system,so it has both
good thinking ability in terms of logic and good thinking ability in terms of images.
出处
《计算机研究与发展》
EI
CSCD
北大核心
1993年第8期24-29,共6页
Journal of Computer Research and Development
基金
国家863自然科学基金国家自然科学基金
关键词
神经网络
符号
专家系统
artificial intelligence
symbolic inference system
neural network
hybrid system.
