【作者】 李荣花；

【导师】 许少华；

【作者基本信息】 大庆石油学院 ， 计算机应用技术， 2003， 硕士

【摘要】 粗集理论是近年来智能信息处理领域发展起来的热门学科之一，是处理不完整、不精确信息的有力工具。它在数据挖掘、专家系统、模式识别、图像处理、机器学习、神经网络等领域发挥了重要作用，其理论和应用技术已经成为许多领科学领域的重要研究课题，并以它特有的优势受到高度的重视。 本文对神经网络和粗集理论的研究现状、发展趋势及应用领域进行了综述，阐述了人工神经网络、粗集理论和粗神经网络的基本概念、基本模型、算法。研究并实现了一种粗神经网络模型，这种粗神经网络采用了粗糙神经元，在网络结构上与经典神经网络有所区别，其学习效率比传统神经网络高。针对神经网络中的学习样本复杂性问题，采用粗集理论中的知识简化思想实现了学习样本的筛选；针对所建立的粗神经网络学习中存在的当学习样本规模较大、不同模式样本的特征差异较小时存在着收敛速度慢，容易陷入局部极小值的缺点，采用遗传算法，对网络的学习进行了优化，优化后的网络收敛速度快，稳定性高，具有较强的推广能力。 根据对粗神经网络的研究成果，并结合大庆油田采油八厂科研项目，研制开发出《基于粗神经网络的沉积微相识别系统》，实现了沉积微相的自动识别。更多还原

【Abstract】 Rough set theory has developed into one of the hot subjects in the area of intelligent information processing, which is a powerful tool to deal with incomplete and unversed information .It performs well in data mining, expert system, pattern recognition, image manipulation, machine learning, neural networks, etc. Its theory and application technologies have become an important research task in many science areas and gain much attention for its special superiorities.Actual research state, development trend and application areas of neural networks and rough set theory are summarized in this paper. Basic concepts, model and algorithms of neural network, RS and RNN (rough neural network) are also set forth in this paper .A kind of RNN model is established. It consists of rough neurons, which is the difference between RNN and typical neural networks. And its learning efficiency is higher than that of typical neural network. To the question of learning pattern complexity, the idea of knowledge simplifying is introduced to filter learning patterns .To the question of low convergence rate and defect of tending to get into local extremum existing in the learning procedure of RNN when pattern size is large and property difference of various patterns is small, genetic algorithm is adopted to optimize learning algorithm. The optimized network has rapid convergence rate and better generalization ability.In light of the combination of research production on RNN and the scientific research project of the eighth oil recovery factory in Daqing oil fields, the sedimentary facies recognition system is developed based on RNN, which realizes auto recognition of sedimentary facies.更多还原