scholarly journals A training algorithm for optimal margin classifiers

1992 ◽  
Author(s):  
Bernhard E. Boser ◽  
Isabelle M. Guyon ◽  
Vladimir N. Vapnik
Keyword(s):  
Training Algorithm

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

Fast Adaboost Training Algorithm by Dynamic Weight Trimming

2009 ◽  
Vol 32 (2) ◽  
pp. 336-341 ◽  
Author(s):  
Hui-Xing JIA ◽  
Yu-Jin ZHANG
Keyword(s):  
Training Algorithm ◽  
Dynamic Weight ◽  
Weight Trimming

An Improved Training Algorithm for Quantum Neural Networks

2014 ◽  
Vol 35 (7) ◽  
pp. 1630-1635
Author(s):  
Yi-peng Zhang ◽  
Liang Chen ◽  
Huan Hao
Keyword(s):  
Neural Networks ◽  
Training Algorithm

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

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