Adaptive parameter based Particle Swarm Optimisation for accelerometer calibration

Dhalwar, Suraj; Kottath, Rahul; Kumar, V. K.; Raj, Alex Noel Joseph; Poddar, Shashi

doi:10.1109/icpeices.2016.7853718

Cited by 9 publications

References 13 publications

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Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting

2023

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In this work, we intend to propose multiple hybrid algorithms with the idea of giving a choice to the particles of a swarm to update their position for the next generation. To implement this concept, Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Whale Optimization Algorithm (WOA) have been utilized. Exhaustive possible combinations of these algorithms are developed and benchmarked against the base algorithms. These hybrid algorithms have been validated on twenty-four well-known unimodal and multimodal benchmarks functions, and detailed analysis with varying dimensions and population size is discussed for the same. Further, the efficacy of these algorithms has been tested on short-term electricity load and price forecasting applications. For this purpose, the algorithms have been combined with Artificial Neural Networks (ANNs) to evaluate their performance on the ISO New Pool England dataset. The results demonstrate that hybrid optimization algorithms perform superior to their base algorithms in most test cases. Furthermore, the results show that the performance of CSA-GWO is significantly better than other algorithms.

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Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting

2023

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A Field Calibration Method for Low-Cost MEMS Accelerometer Based on the Generalized Nonlinear Least Square Method

Hassan

Bao

2020

Multiscale Sci. Eng.

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This paper proposes a field calibration method for an accelerometer without the need of having any external devices for calibration. In the proposed calibration method, generalized nonlinear least-square (GNLS) is used to estimate deterministic errors. A novel sensor's data collection procedure is developed to collect data of an accelerometer along all three axes and all possible orientations where the expectation of influence of all possible errors is very high. The proposed calibration method is verified by applying it to two different accelerometers. The proposed calibration method achieved an accurate estimation of calibration parameters. The results of the proposed GNLS based calibration method are compared with two other commonly used algorithms, such as Levenberg-Marquardt (LM) and Gauss-Newton (GN). Simulation and experimental results show that the proposed GNLS-based calibration method is slightly more accurate than the LM and GN. The GNLS convergence rate for estimating the calibration parameters is also faster than the LM and GN.

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An ensemble approach to meta-heuristic algorithms: Comparative analysis and its applications

Singh

Kottath

2021

Computers & Industrial Engineering

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Adaptive parameter based Particle Swarm Optimisation for accelerometer calibration

Cited by 9 publications

References 13 publications

Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting

Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting

A Field Calibration Method for Low-Cost MEMS Accelerometer Based on the Generalized Nonlinear Least Square Method

An ensemble approach to meta-heuristic algorithms: Comparative analysis and its applications

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