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论文标题:自适应模糊系统在手写体数字识别中的应用研究
作者:张镭
作者专业:计算机软件人工智能
导师姓名:黄战
授予学位:硕士
授予单位:暨南大学
授予学位时间:19990501
论文页数:59页
文摘语种:中文文摘
分类号:TP18 TP391.4
关键词:手写体数字 自适应 模糊逻辑 神经网络 模式识别
摘要:该文针对模式识别的特点,构造了适合于模式识别问题的自适应模糊系统,对三种不同学习算法加以改进,在手写全数字识别上对分类器进行了实现,并与采用BP算法训练的三层前馈神经网络分类器相比较,分析其优劣.仿真实验表明,在该文的样本集条件下,自适应模糊分类吕的识别性能优于神经网络分类器,这充分体现了自适应模糊技术用于数字识别的优越性和潜力.-thesis entitled : adaptive fuzzy system in a handwritten numeral recognition of applied research Author : Zhang Lei professional author : artificial intelligence computer software instructor Name : Huang war conferred degrees : Master award units : Jinan University conferred degrees : 19990501 page thesis : Abstracts 59 languages : Chinese Digest Key words : TP18-image Keywords : handwritten digital adaptive fuzzy logic neural network pattern recognition Abstract : In this paper the characteristics of pattern recognition, suitable for the construction of adaptive pattern recognition fuzzy systems, three different learning algorithm to improve the handwriting recognition on digital classification of the device to achieve, BP and with a three-tiered training algorithm for neural network clas
作者:张镭
作者专业:计算机软件人工智能
导师姓名:黄战
授予学位:硕士
授予单位:暨南大学
授予学位时间:19990501
论文页数:59页
文摘语种:中文文摘
分类号:TP18 TP391.4
关键词:手写体数字 自适应 模糊逻辑 神经网络 模式识别
摘要:该文针对模式识别的特点,构造了适合于模式识别问题的自适应模糊系统,对三种不同学习算法加以改进,在手写全数字识别上对分类器进行了实现,并与采用BP算法训练的三层前馈神经网络分类器相比较,分析其优劣.仿真实验表明,在该文的样本集条件下,自适应模糊分类吕的识别性能优于神经网络分类器,这充分体现了自适应模糊技术用于数字识别的优越性和潜力.-thesis entitled : adaptive fuzzy system in a handwritten numeral recognition of applied research Author : Zhang Lei professional author : artificial intelligence computer software instructor Name : Huang war conferred degrees : Master award units : Jinan University conferred degrees : 19990501 page thesis : Abstracts 59 languages : Chinese Digest Key words : TP18-image Keywords : handwritten digital adaptive fuzzy logic neural network pattern recognition Abstract : In this paper the characteristics of pattern recognition, suitable for the construction of adaptive pattern recognition fuzzy systems, three different learning algorithm to improve the handwriting recognition on digital classification of the device to achieve, BP and with a three-tiered training algorithm for neural network clas
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