Design of artificial neural network using particle swarm optimisation for automotive spring durability
- 6 November 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in Journal of Mechanical Science and Technology
- Vol. 33 (11), 5137-5145
- https://doi.org/10.1007/s12206-019-1003-9
Abstract
No abstract availableKeywords
This publication has 28 references indexed in Scilit:
- Revisiting the critical values of the Lilliefors test: towards the correct agrometeorological use of the Kolmogorov-Smirnov frameworkBragantia, 2014
- The vibrations induced by surface irregularities in road pavements – a Matlab® approachEuropean Transport Research Review, 2013
- Design a PID Controller for Suspension System by Back Propagation Neural NetworkJournal of Engineering, 2013
- Optimization of Semi-active Suspension System Using Particle Swarm Optimization AlgorithmAASRI Procedia, 2013
- Evaluation of Fatigue Tests by Means of Mathematical StatisticsProcedia Engineering, 2012
- A general class of zero-or-one inflated beta regression modelsComputational Statistics & Data Analysis, 2012
- A modification of Morrow and Smith-Watson-Topper mean stress correction modelsFatigue & Fracture of Engineering Materials & Structures, 2011
- Design and failure modes of automotive suspension springsEngineering Failure Analysis, 2008
- An improved PSO-based ANN with simulated annealing techniqueNeurocomputing, 2005
- Neural Network Control for a Semi-Active Vehicle Suspension with a Magnetorheological DamperJournal of Vibration and Control, 2004