基于神经网络的不变性模式识别的人脸识别系统

文章提出了一种优化的多层神经网络,能完成对图像不变性模式识别。基于此多层神经网络开发了人脸识别系统。仿真实验表明该人脸识别系统,具有较高的识别速度、准确率、容错性和鲁棒性,并且基本解决了开发实用化的人脸识别系统所面临的问题,即模式识别不变性的问题。     

Noisy Time-Series Prediction using Pattern Recognition Techniques

Time-series prediction is important in physical and financial domains. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This paper describes a Pattern Modelling and Recognition System (PMRS) which is used for forecasting benchmark series and the US S&P financial index. The main aim of this paper is to evaluate the performance of such a system on noise free and Gaussian additive noise injected time-series. The results show that the addition of Gaussian noise leads to better forecasts. The results also show that the Gaussian noise standard deviation has an important effect on the PMRS performance. PMRS results are compared with the popular Exponential Smoothing method.     

基于BP网络的字符模式识别

神经网络理论已经成为解决某些问题的重要手段的方法.但利用神经网络进行解决问题和设计的时候,必定会涉及到大量的有关数值计算等问题,所以利用计算机对神经网络模型进行仿真和辅助设计时,仍是件很麻烦的事情.所以MATLAB的便利受到了青睐,BP网络在人工神经网络中应用最为广泛,而且在理论上十分完善,网络结构也比较直观.在BP网络中,模式识别是应用比较广泛的一个方面.该系统使用MATLAB中神经网络工具箱对英文表中的26个字母进行识别.通过建立网络,训练网络,测试网络,最后进行仿真,完成了正确识别26个英文字母的功能.该系统的操作界面简洁、友好、使用简单方便.     

基于BP小波网络的故障模式识别

提出了一类新的BP小波网络，该网络采用BP学习算法，可实现信号的小波变换、特征提取和模式分类，具有结构清晰、算法简便的特点。将该网络应用于柴油机的活塞-缸套故障模式识别，并与用一般BP网络识别的结果比较，表明该网络对于非平稳时变振动信号具有很好的模式识别能力。     

一种复杂模式识别问题的求解方法

针对复杂的模式识别问题,提出了一种用串－并行混合结构的多种神经网络模型进行求解的方法。首先用ART网络对训练集中的样本进行粗分类,以减小训练集的样本规模,然后用多个BP网络并行地对小训练集进行训练。用于汉字识别问题,取得了很好的效果。     

A Fuzzy Petri Net for Pattern Recognition: Application to Dynamic Classes

When involving evolutionary natural objects, the odeling of dynamic lasses is the main issue for a pattern recognition system. This problem an be avoided by making dynamic the syste of pattern recognition which an then enter into various states according to the evolution of the lasses. We propose a dynamic recognition system founded on two types of learning. The static aspect of the learning is ensured by lassifiers or systems of lassifiers, while the dynamic aspect is translated by the learning of the planning of the various states by a fuzzy Petri net. The method is sucessfully applied to a synthetic data set. Received 21 September 2000 / Revised 19 December 2000 / Accepted in revised form 1 March 2001     

模式识别系统中的一次性风险判据问题

该文讨论模式识别系统，当备选方案的损失代价相互矛盾时，进行一次性风险决策的判据问题，给出选择风险最小方案时的判据条件及风险测评方法。提出决策面谨慎度位于Neyman-pearson判决准则和极小化极大判据之间的最小风险估计函数∪（1,p,B)及其具体表达形式。通过例子说明这一函数在风险决策中的应用及其在利用计算机仿真确定分类样本最佳损失矩阵中的重要作用。     

A Hierarchical Self-orginising Map Model for Sequence Recognition

This paper presents an analysis of an original hierarchical neural model on a complex sequence - the complete sixteenth fourpart fugue in G minor of the Well-Tempered Clavier (vol 1) of J. S. Bach. The model makes an effective use of context information, through its hierarchical topology and embedded time integrators, and that enables it to keep a very good account of past events. The model performs sequence classification and discrimination efficiently. It has application in domains which require pattern recognition, or particulary, which demand recognising either a set if sequences of vectors in time, or sub-sequences into a unique and large sequence of vectors in time. Received: 16 March 1999, Received in revised form: 30 July 1999, Accepted: 27 September 1999     

神经网络听力检测分类实验系统

该文介绍一种用人工神经网络方法检测、区分兔子听觉系统是否正常的分类系统，进而仿真检测人类的听觉系统。文章介绍了系统的实现方法，BP网的网络算法，识别结果及分析。     

一个印刷体汉字识别系统的设计

给出一个印刷全汉字识别系统的设计方案，它主要包括扫描输入，模糊增强与聚类分割 ，图象数据二值比，通过并行神经网络进行汉字匹配等四个步骤。     

神经网络数据融合模式识别系统性能研究

有效地利用多传感器的信息构建数据融合系统后，其性能优于单传感器系统。该文针对一种多传感器神经网络数据融合模式识别系统，对其分类性能进行了研究，以信息论的观点，从理论上证明了上述结论的正确性。     

基于多Agent客户识别的反洗钱系统研究

针对现行的反洗钱系统的缺陷,该文将模式识别Agent引入反洗钱系统设计,充分发挥Agent系统自主性、反应性、协作性的特点,构造了基于多Agent客户识别的反洗钱系统模型。并对多Agent通信、协商、实现与支持向量机模式识别等关键技术进行了研究。从而为反洗钱系统的实现提供了支持。     

模式识别与智能系统研究展望和对策

分析了自动化技术发展中的智能化趋势,具体阐明了模式识别与智能系统正在成为推动自动化领域发展的核心科学技术.通过一些研究和应用的例子说明国外在模式识别与智能系统方面的发展状况.最后指出我国在自动化领域面临的挑战和对策.     

Financial Forecasting Using Support Vector Machines

The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE), Mean Absolute Error (MAE), Directional Symmetry (DS), Correct Up (CP) trend and Correct Down (CD) trend. S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial time series.     

一种模块化神经网络结构用于模式识别

在模式识别中,通常直接用神经网络来处理复杂的多类分类问题,其识别的误判率较大。该文基于任务分解与模块整合的思想,提出了一个模块化Kohonen神经网络(KTD)结构用于模式分类,给出了其学习方法并做了模拟仿真,模拟仿真表明KTD能够获得较高的识别率且误判率较小。     

一种适用于模式识别的新型神经网络

提出了一种适用于模式识别的新型神经网络模型——局部有监督特征映射网络,描述了该网络的拓扑结构和学习算法,研究了网络的基本性能,最后将其应用到了质量控制图的模式识别中。理论研究和仿真实验表明,该网络结构简单、算法简洁,收敛速度快、识别精度高,适用于需要大样本训练、随机干扰严重的复杂模式的分类与识别。     

神经网络在轧钢加热炉模式识别与智能控制中的应用

本文分析研究了神经网络的工作原理及其在加热炉模式识别与智能控制系统中应用情况，论述了加热炉模式识别与智能控制的特点。实践结果表明，这种控制思想是可行的．能够取得满意的控制效果。     

Forecasting Financial Time Series using Neural Network and Fuzzy System-based Techniques

Forecasting currency exchange rates are an important financial problem that is receiving increasing attention, especially because of its intrinsic difficulty and practical applications. During the last few years, a number of nonlinear models have been proposed for obtaining accurate prediction results, in an attempt to ameliorate the performance of the traditional linear approaches. Among them, neural network models have been used with encouraging results. This paper presents improved neural network and fuzzy models used for exchange rate prediction. Several approaches, including multi-layer perceptions, radial basis functions, dynamic neural networks and neuro-fuzzy systems, have been proposed and discussed. Their performances for one-step and multiple step ahead predictions have been evaluated through a study, using real exchange daily rate values of the US Dollar vs. British Pound. ID="A1" Correspondence and offprint requests to: Dr V. Kodogiannis, Mechatronics Group, Department of Computer Science, University of Westminster, London HAI 3TP, UK. Email: kodogiv@wmin.ac.uk