摘要
神经网络是目前处理科技领域各类问题的一个重要工具,它由大量简单单元以及这些单元的分层组织大规模联结而成,力图像生物神经系统一样处理事物;BP网络采用传播算法,是目前应用最为广泛和可靠的神经网络之一,具有较强的分类和学习能力。从模式识别出发,在选取典型实例的基础上,建立BP网络算法模型,对算法进行动态误差修正的改进,提高了算法的收敛速度,并根据算法流程,通过运用Matlab软件对其进行仿真验证,说明了BP网络算法在模式识别中具有应用可行性。
Neural network is an important tool for processing technology fields of various kinds of issues, which is composed by a large number of simple units and the hierarchical organization of mass coupling of these units. Force diagram biological deal with things like neural system; BP network use propagation algorithm, which is one of the most widely used and reli-able neural networks with strong classification and learning ability. Based on pattern recognition and selection of typical ex-amples, the paper was established BP network algorithm model, improved dynamic error correction of algorithm, which pro-moted rate of convergence of algorithm. According to the algorithm process, the paper was explained feasibility of applica-tions of BP network algorithm in pattern recognition through using the Matlab software simulation.
出处
《科技创新与生产力》
2014年第1期79-81,共3页
Sci-tech Innovation and Productivity
关键词
BP网络
学习算法
模式识别
BP network
learning algorithm
pattern recognition
