A Neural Network-Augmented Bayesian Approach To Uncertain Parameter Estimation In Nonlinear Dynamic Systems
type: CreativeWork
description: The objective of this research is to develop a new methodology by combining Artificial Neural Networks and Bayesian approach which utilizes kinematic quantities of a nonlinear dynamic system to estimate uncertain and unknown parameters more accurately with reduced estimation error and using fewer iterations. Kinematics pertains to the motion of bodies in the robotic mechanism without regard to the forces or torques that cause the motion. In this study, a new methodology which is the combination of a heuristic method (Neural Network) and Bayesian Approach (Particle Markov Chain Monte Carlo) is developed to determine and estimate the unknown system parameters with high accuracy, in more efficient way with fewer iteration number. The new methodology can reduce the iteration number in Bayesian samplers’ algorithms and maintains the estimation accuracy, therefore it could make the algorithm less computationally expensive and demanding. At the accuracy level of 0.002, the result showed the average of 33.64% improvement in proposed method compared to the regular PMH sampler, At the accuracy level of 0.001, the average of improvement was 34.99% in proposed methods compared to the regular PMH sampler and finally and finally, at the accuracy level of 0.0005, the result shows the average of 32.34% improvement in proposed method compared to the regular PMH sampler.
abstract: The objective of this research is to develop a new methodology by combining Artificial Neural Networks and Bayesian approach which utilizes kinematic quantities of a nonlinear dynamic system to estimate uncertain and unknown parameters more accurately with reduced estimation error and using fewer iterations. Kinematics pertains to the motion of bodies in the robotic mechanism without regard to the forces or torques that cause the motion. In this study, a new methodology which is the combination of a heuristic method (Neural Network) and Bayesian Approach (Particle Markov Chain Monte Carlo) is developed to determine and estimate the unknown system parameters with high accuracy, in more efficient way with fewer iteration number. The new methodology can reduce the iteration number in Bayesian samplers’ algorithms and maintains the estimation accuracy, therefore it could make the algorithm less computationally expensive and demanding. At the accuracy level of 0.002, the result showed the average of 33.64% improvement in proposed method compared to the regular PMH sampler, At the accuracy level of 0.001, the average of improvement was 34.99% in proposed methods compared to the regular PMH sampler and finally and finally, at the accuracy level of 0.0005, the result shows the average of 32.34% improvement in proposed method compared to the regular PMH sampler.
author: Zakeri, Roja
keywords: Applied mathematics, Engineering, Robotics
date published: 2020-01-01T08:00:00Z
date modified: 2026-07-24T01:40:36Z
provider: Claremont Graduate University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Claremont Graduate University