A Fast Training Method for SAR Large Scale Samples Based on CNN for Targets Recognition

2018 ◽  
Author(s):  
Yuan Zhang ◽  
Yang Song ◽  
Yanping Wang ◽  
Hongquan Qu
Keyword(s):  
Large Scale ◽  
Training Method ◽  
Fast Training

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

scholarly journals Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiancheng Liu ◽  
Congxiang Tian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Fast Training ◽  
Simulation Calculation ◽  
Monitoring Platform

With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.

scholarly journals A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiusheng Chen ◽  
Xiaoyu Zhang ◽  
Kai Guo
Keyword(s):  
Large Scale ◽  
Novelty Detection ◽  
Imbalanced Data ◽  
Feature Space ◽  
Training Time ◽  
Angular Region ◽  
The Core ◽  
Large Scale Problems ◽  
Fast Training ◽  
Core Set

A large vector-angular region and margin (LARM) approach is presented for novelty detection based on imbalanced data. The key idea is to construct the largest vector-angular region in the feature space to separate normal training patterns; meanwhile, maximize the vector-angular margin between the surface of this optimal vector-angular region and abnormal training patterns. In order to improve the generalization performance of LARM, the vector-angular distribution is optimized by maximizing the vector-angular mean and minimizing the vector-angular variance, which separates the normal and abnormal examples well. However, the inherent computation of quadratic programming (QP) solver takesO(n3)training time and at leastO(n2)space, which might be computational prohibitive for large scale problems. By(1+ε)  and  (1-ε)-approximation algorithm, the core set based LARM algorithm is proposed for fast training LARM problem. Experimental results based on imbalanced datasets have validated the favorable efficiency of the proposed approach in novelty detection.

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