Recognizing small handwritten character set with this new method and constructing an integrated reorganization system with subspace classifier and bp classifier are the main objects in this paper 本文研究的目的就是将这一新的识别方法应用于手写字符的小字符集识别，并结合神经网络识别方法构造一个实用的集成识别系统。
The chip shapes in the course of cutting processing was carefully analyzed and studied and put forward feature expanded and radbas function neural network algorithms for recognition chip shape , more than experiments program was developed 对于切削加工中的切屑形态进行了分析研究，提出了特征拓展和径向基函数神经网络识别切屑形态的算法，并开发了相应的程序软件。
After analyzing and discussing the results a conclusion is drawn that it is a kind of valid method to realize the automation and intelligence of distinguishing chemical agents using the neural networks combined with chemical sensor 用沙林模拟数据进行了测试和分析，结果表明，利用与化学传感器相联结的神经网络识别毒剂，是实现毒剂识别自动化、智能化的一种有效方法。
This paper revolves around the central task of image identification . it is mainly about collecting and preprocessing the original data of target images , methods of invariable feature extraction and the identification technology of the artificial neural network 本文围绕图像识别这一个中心课题，研究了目标图像原始数据的获取、预处理、不变性特征的提取方法以及神经网络识别技术。
4 . choose the neural network method , join with the characteristic parameter which taken out from the log curve , write a recognition programmed of reservoir neural network , and make a treatment of practical log data , to raise the suit rate of computer recognition for reservoir ( 4 )选用神经网络方法，结合所提取的特征参数，编写储层神经网络识别程序，并进行实际测井资料处理，以提高储层计算机识别的符合率。
Then , moment feature which is input in artificial nerusl network is used to recognize aircraft . in this paper , mainly improved back - propagation and radial basis function recognition methods . also do some improvement to these method and analyse the result 接着是对目标进行识别,将矩特征输入神经网络进行模式识别和分类。主要采用了bp神经网络、基于径向基函数神经网络识别方法,并在此基础上有所改进,对识别效果进行了对比分析。
It is noted from our study that smoothing the finger prints with wavelet is great benefit to the pattern recognition . we found from the result that the identification rate to the laboratory data is above 90 % , to the field data , the identification rate is about 80 % , and to the data assembled from laboratory and field , the identification rate is about 80 % 经bp网络识别发现，对于实验室的95个样本，人工神经网络的识别的率可达90 %以上；而对现场的92个样本，其识别率为80 %左右；对实验室和现场的所有数据融合后的206个样本，识别率可达80 %左右。
The application of rough sets theory and methods in neural network technology is studied . after combined rough sets theory with the neural network technology , the neural network recognition system based on rough set theory is advanced , in which rough sets theory is used to determine the number of neural network ' s input nodes . the complexity of neural network ' s structure is reduced 论文对粗糙集理论及方法在神经网络技术中的应用进行了研究，从而将粗糙集理论与神经网络技术相结合，提出了基于粗糙集理论的神经网络识别系统，利用粗糙集理论来确定神经网络的输入节点数，降低了神经网络的结构的复杂性，给出了该算法的详细步骤。
The results of research show that the mda function and 5 - indices - ann model can successfully identify the fraudulent accounting information . particularly , 70 percent accuracy can be achieved using the latter model in identifying fraud of listed companies other than the original samples . the outcome of the research will provide academic and practical support to accounting firm , investors and government departments 研究结果表明多元判别分析识别函数和5指标神经网络识别模型对会计信息失真具有较强的识别能力,特别是后者应用于中国上市公司的会计信息失真识别可取得高于70 %的准确性,这一成果将为会计事务所、投资者和监管部门提供相关理论支撑和技术支持。
The other one is the synthetical local nonlinear pca neural network recognition model constructed by combining the nonlinear generalization of pca and sub - space pattern recognition technology . we use the two recognition systems in handwritten digitals and characters recognition and obtain some satisfactory results . compared with some traditional classifiers , our systems have better recognition performances 而基于非线性pca的神经网络识别模型对传统的线性pca进行了推广，并利用了子空间的模式识别方法，针对每个字符类使用神经网络建立多个模板，然后利用pca神经网络和聚类算法构造自动编码器组对模式类进行重构，避免了特征提取的复杂性和信息的丢失，提高了系统的识别性能和运算效率。
