Load Cell is used to evaluate unknown objects ' weight. It presents noise at the output due to different inner and external variables. The output deviates from the required response. This project's primary goal is to use Adaptive and Approximation methods to rectify a load cell's output reaction. Approximation is used to generate the reference or training signal at first using Approximation techniques. To generate the training signal, Least Square Approximation (LSA) and Particle Swarm Optimization (PSO) techniques are used and optimized to the desired value. This training signal is later used in an adaptive scheme as a reference signal. Adaptive methods are used to correct the load cell's output reaction. In the adaptive filter, Least Means Square Algorithms are used to remove the noisy load cell output with the adaptive filter. The noise is primarily caused by the creeping and drifting mistake at the output. The Adaptive Filter utilizes the reference signal produced by approximation methods to eliminate both creeping and drifting errors and to produce a load cell's required reaction.
Comparison of Mathematical Approximation Methods for Mine Ventilation Network Analysis
