feed-forward neural network中文是什么意思

• 反向传输神经网络

• feed    vt. 1.给…饮食，给…东西吃；给(婴儿)喂奶。 2. ...
• forward    adv. 1.向前，前进 (opp. backward) ...
• neural network    【计算机】模拟脑神经元网络〔一种模拟人脑神经细胞的电脑系 ...
• neural network    【计算机】模拟脑神经元网络〔一种模拟人脑神经细胞的电脑系统〕。
• feed forward    向前馈送，前馈; 正向输送
• forward feed    顺流进料; 顺流送料, 顺向进料; 向前进料
• forward-feed    (录像或磁盘)向前推进
• alternating projection neural network    交替投影神经网络
• ann artificial neural network    人工神经网络
• articificial neural network    类神经网络
• artificial neural network    类神经网路; 人工神经网络
• artificial neural network (ann)    类神经网络
• artificial neural network system    人工神经元网络系统
• biological neural network    神经网络
• chaotic neural network    混沌神经网络
• cnn cellular neural network    蜂窝神经网络
• cnn computational neural network    计算的神经中枢网
• connectionist neural network    连接机制神经网络
• european neural network society    欧洲神经网络学会; 欧洲神经元网络
• fnn fuzzy neural network    模糊神经网络
• fuzzy neural network    模糊神经网络
• fuzzy-neural network    模糊神经网络
• higher order neural network    高阶神经网络
• hnn hybrid neural network    混合神经网络
• hopfield neural network    霍普菲尔德神经网络

• 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。
• Neural networks can be viewed as a universal approximator for nonlinear functions , but the multi - layer feed - forward neural network which be used usually is a static state network in nature , it is disagree with the real - time identification for dynamic system . moreover , recurrent neural networks can simulate the state memory mechanism of dynamic system , so it can be utilized as the model of dynamic time delay system
  神经网络具有逼近任意连续非线性函数的能力，但常用的多层前馈式反传网络本质上是一种静态网络，不适合动态系统的实时辨识，而递归神经网络能够实现对动态系统状态记忆机制的模拟，因此更适合于作为动态时延系统的模型。
• This method took the robot actual poses and corresponding joint errors as inputs and outputs of a feed - forward neural network respectively , so as to achieve the real - time joint errors in arbitrary pose through the neural network , and pose accuracy was improved only through correcting the joints angles
  这种标定方法把机器人实际位姿和相应的关节角误差分别作为前馈神经网络的输入和输出来训练网络，从而获得机器人任意位姿时的关节角误差值，通过修改关节值来提高机器人的位姿精度。
• The first algorithm based on feed - forward neural network , this algorithm is developed by the combination of conventional cma and feed - forward neural network . according to the cma blind equalization thought - way , a new cost function is proposed so that apply the feed - forward neural network to blind equalization
  第一类是基于前馈神经网络( fnn )盲均衡算法，这种算法是将传统的cma算法与神经网络相结合提出的，根据传统算法的均衡思想而重定义了代价函数，使其能用到神经网络当中。