In order to further video analysis , an algorithm of abrupt shot boundary detection based on fuzzy clustering neural network ( fcnn ) is proposed , and it has the advantages of high precision as well as robust to fast move . caption segmentation is the key to the whole process , fcnn can also be utilized to locate caption region , however , the technique is time - consuming . thus an improved projection segmentation method is presented , and the experimental results show that it is simple and practical , and fits for real - time processing 为了便于后续的视频分析，提出了一种基于模糊聚类神经网络( fcnn )的镜头突变检测算法，实现视频镜头分割，该算法具有检测精度高、对运动稳健等优点；区域定位是字幕提取的关键一环，同样利用fcnn分类器可实现字幕定位，但其运算量大，定位精度不高，因此提出了一种改进的投影分割方法实现字幕区域定位分割，实验表明其简单实用，适于实时处理；考虑到单个字符背景相对简单，为此提出了一种基于单字符的字幕二值化算法，最终在经由字符分割、二值化及残留背景像素清除之后，得到了清晰、高质的字幕图像，字符识别结果证明了这一点。
Chapter 4 discusses how to stabilize a class of time - delays neural networks via standard feedback control ; this kind of neural networks provides a unified view of several well - known neural networks ( such as hopfield neural networks and cellular neural networks ) with discrete delays or distributed delays . a stability criterion is given by using the lyapunov method . all the results obtained in this chapter are stated in simple algebraic forms so that their verifications and applications are straightforward and convenient 通过使用lyapunov方法,我们得到了一类时延神经网络经过标准反馈控制后的稳定条件,这类神经网络涵盖了几种著名的神经网络,如hopfield神经网络、细胞神经网络,同样,本章所得到的结论也是简单的代数表达式,使用非常方便,最后在用计算机通过做数值实验对这些结论进行了验证。
But all the models of neural networks before are assumed that self - feedback is linear which can not reflect the essentiality of the neural networks . so in chapter two of this paper , the self - feedback is improved and can be nonlinear . moreover , basic features of this kind of neural networks are investigated 但是以往所提出的该类神经网络模型都假设自反馈项为线性的，不能真实的反映其本质，所以在本文的第二章将对原有的动态神经网络模型进行推广，并研究推广模型的一些基本特性。
The study of the dynamical behavior of feedback neural networks has been an important topic in the neural - network field since the hopfield network model was proposed in 1982 which demonstrated the great potential of the model in applications of optimization , associative memory , signal processing , image processing , pattern recognition , etc . , to name just a few 自1982年hopfield模型提出以来，动态行为的研究一直是反馈型神经网络理论研究的重点。主要原因在于这类神经网络的应用(如优化、联想记忆、信号处理、图像处理以及模式识别等)都与网络的动态特性有关。
