Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests
Published 2016 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests
Authors
Keywords
-
Journal
SENSORS
Volume 16, Issue 3, Pages 351
Publisher
MDPI AG
Online
2016-03-10
DOI
10.3390/s16030351
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Gaussian-PSO with fuzzy reasoning based on structural learning for training a Neural Network
- (2016) Haydee Melo et al. NEUROCOMPUTING
- Forecast of off-season longan supply using fuzzy support vector regression and fuzzy artificial neural network
- (2015) Komgrit Leksakul et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- A self-adaptive harmony PSO search algorithm and its performance analysis
- (2015) Fuqing Zhao et al. EXPERT SYSTEMS WITH APPLICATIONS
- Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China
- (2015) Wenxi Lu et al. JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA
- Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model
- (2015) Qichun Bing et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning
- (2015) Datong Liu et al. MEASUREMENT
- Wind speed prediction using reduced support vector machines with feature selection
- (2015) Xiaobing Kong et al. NEUROCOMPUTING
- Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network
- (2015) Zhuomin Wang et al. Water Science and Technology-Water Supply
- A multi-scale relevance vector regression approach for daily urban water demand forecasting
- (2014) Yun Bai et al. JOURNAL OF HYDROLOGY
- Artificial Neural Network estimation of wheel rolling resistance in clay loam soil
- (2013) Hamid Taghavifar et al. APPLIED SOFT COMPUTING
- Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis
- (2013) Davor Z. Antanasijević et al. International Journal of Greenhouse Gas Control
- Investigating the effect of velocity, inflation pressure, and vertical load on rolling resistance of a radial ply tire
- (2013) Hamid Taghavifar et al. JOURNAL OF TERRAMECHANICS
- Short-term wind power prediction using differential EMD and relevance vector machine
- (2013) Yan Bao et al. NEURAL COMPUTING & APPLICATIONS
- A Relevance Vector Machine-Based Approach with Application to Oil Sand Pump Prognostics
- (2013) Jinfei Hu et al. SENSORS
- Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks
- (2012) Víctor Martínez-Martínez et al. SENSORS
- In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines
- (2012) D. Anguita et al. IEEE Transactions on Neural Networks and Learning Systems
- Optimal the tilt angles for photovoltaic modules using PSO method with nonlinear time-varying evolution
- (2010) Ying-Pin Chang ENERGY
- Combined modeling for electric load forecasting with adaptive particle swarm optimization
- (2010) Jianzhou Wang et al. ENERGY
- Experimental study and analysis on driving wheels’ performance for planetary exploration rovers moving in deformable soil
- (2010) Liang Ding et al. JOURNAL OF TERRAMECHANICS
- A review on traction prediction equations
- (2009) V.K. Tiwari et al. JOURNAL OF TERRAMECHANICS
- Slip sinkage effect in soil–vehicle mechanics
- (2009) Modest Lyasko JOURNAL OF TERRAMECHANICS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started