feed-forward neural network造句
例句与造句
- Feed - forward neural network based on gaussnewton - nl2sol algorithm and its application
法的前馈神经网络及应用 - Chapter 2 focuses on weight learning and structure learning of multi - layer feed - forward neural networks
主要研究前向神经网络的权值学习和结构学习方法。 - This paper is mainly devoted to the principle and the implemental algorithms of qualitative learning for feed - forward neural networks ( fnns )
本文主要研究前向神经网络定性学习的原理和学习算法。 - Wavelet neural network ( wnn ) is a novel feed - forward neural network based on wavelet theory , and it possesses several excellent features
摘要小波神经网络是建立在小波理论基础上的一种新型前馈神经网络，具有许多优良特性。 - Based on rbf neural network and perceptron neural network , a four - layer feed - forward neural network named radial basis perceptron ( rbp ) network is presented
基于rbf网络和感知器( perceptron )网络建立一四层前馈神经网络?径向基感知器( radialbasisperceptron ， rbp )网络。 - It's difficult to find feed-forward neural network in a sentence. 用feed-forward neural network造句挺难的
- The feed - forward neural network is provided to recognizing the currency values , which makes use of the capability to extract features automatically and the error tolerance of neural networks
利用神经网络自动特征提取能力和容错特性，提出使用前馈神经网络对面值进行识别。 - Firstly , studied feed - forward neural network and put forward a new algorithm on bp network , called bp algorithm based on robust error function ( bparef ) , and the algorithm is proved to be effective for approaching nonlinear system
首先研究了前馈神经网络，提出了基于鲁棒误差函数的bp神经网络的算法，并且验证了其对非线性系统逼近的有效性。 - In the last of this paper we apply our algorithms to the learning of feed - forward neural network , and get some new learning algorithms . we also give some numerical experiments to compare our algorithms with others
最后，将得到的这些优化加速收敛方法应用到了多层前馈神经网络的学习过程，给出了加速收敛的bp算法，通过实际神经网络学习问题验证了工作的成效。 - Recently a covering method for feed - forward neural network design has been proposed by professor zhang ling . based on the sphere neighborhood model , this method transforms the design of neural classifiers to a geometrical covering problem
近来张铃教授提出了一种前馈神经网络设计的覆盖方法，它以球面领域模型为基础，使得神经网络的设计转化为几何覆盖问题。 - To overcome the limitations of general fnns and bp algorithm , this thesis introduced a hybrid feed - forward neural network , which is composed of a linear model and a general multi - layer fnn , and proposed a new learning algorithm for the hybrid fnn
其次，针对bp网络存在的缺陷，结合前向神经网络和线性最小二乘法的优点，构造了一种基于混合结构的神经网络，提出了相应的非迭代的快速学习算法。 - In this thesis , some kinds of learning algorithms of feed - forward neural network have been analyzed ; later a scaled conjugate gradient algorithm is presented to train network , furthermore , modified training methods have been provided to improve the neural network performance
在分析比较了几种前馈神经网络的学习算法后，提出尺度共轭梯度算法对网络进行训练，并针对现有网络训练方法提出了改进。 - This thesis , based on extraction and analysis on the feature of currency , presents the feed - forward neural network to recognizing the currency values , combines with the statistical analysis to recognize the fake and forms a set of methods for currency recognition
本文在对货币特征精确提取和分析的基础上，提出将前馈神经网络应用于货币识别问题，并结合统计分析方法，形成一套货币识别的系统方法。 - Based on the analysis and processing of the digital speckle pattern , the translation and rotation invariant features are discovered , and a one - step feed - forward neural network is creatively proposed which makes it possible to realize the intelligent recognition of interface defects
通过对数学散斑条纹的分析与处理，找出了能代表条纹信息的移位不变与旋转不变特征值? ?最大斜率，进而构造一种新的网络模型，即单步前馈式三层网络系统.率先实现了把神经网络系统用在数字散斑无损检测之中，完成了神经网络系统对粘接界面缺陷的智能辨识 - First , this thesis reviewed the development and applications of anns , and it presented some typical anns which are widely used . especially , it analyzed multi - layer feed - forward neural networks ( fnns ) and their traditional learning algorithm , back propagation ( bp ) algorithm , because fnns are the most common used anns
本文首先介绍了神经网络理论的发展历史和应用领域，然后介绍了人工神经元网络的特点及目前应用比较广泛的几种典型的神经网络，并重点分析了当今最流行的bp网络的特性。 - Later on , after elaborating the disadvantages of the old methods in detecting and recognizing moving objects , a series of corresponding approaches are proposed , such as grid scan , local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed , maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise , a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera . after that , much emphasis is given on application study in pattern recognition with a feed - forward neural network . both the basic bp algorithm and improved bp algorithm in the study process are described in detail , and the later is used to quicken convergence speed and improve validity of the network
然后，分析和阐明了传统的运动目标检测方法的不足，并在此基础上结合研究中的实际实验环境，提出了一系列解决方法，包括针对降低庞大数据量而提出的网格扫描、局部“跟虫”追踪和动态窗口扫描等目标检测方法，针对实验环境中光照不均和颜色干扰提出基于人机交互的最大最小值阈值选取方法和引入改进的rgb模型到hsv模型的转换方法，为消除图像畸变对识别精度的恶劣影响而采用的通过控制点进行双线性插值进行畸变校正的方法；紧接着，概述了神经网络的发展历史和几种常用神经网络模型的特点，重点研究了前馈型神经网络在模式识别中的应用问题，详细阐述了基本的bp算法和学习过程中bp算法的改进，从而使网络收敛速度更快，解决问题更有效，并在此基础上，设计了一个基于bp神经网络的运动目标识别系统，给出了实验结果。
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