Abstract
Purpose - This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. This study analyzes the inertial weight factor value in the (PSO) algorithm and proposes non-linear weights with decreasing strategy to implement the Improvement PSO (IPSO) algorithm.Design/methodology/approach - The weighted factor is estimated in the multi-sensor data fusion network. This FLAG Programmable System on Chip (SoC) Developing System 1605A (FLAG-the PSoC-1605A) is an experimental platform, using various types of sensors, combined with ZigBee wireless sensor networks, the TCP / IP network.Findings - The IPSO applies the IOT system in monitoring the environment better than other existing PSO methods in computing precise and convergence rate for excellent fusion results.Originality/value - The experimental results show that the IPSO algorithm optimally integrates the weight factor, information source fusion reliability, information redundancy, complementarity and hierarchical structure integration of an uncertain fusion case. The sensor data approximates the optimal way to extract useful information from each fusion data, successfully eliminates noise interference, and produces excellent fusion results.
Purpose - This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. This study analyzes the inertial weight factor value in the (PSO) algorithm and proposes non-linear weights with decreasing strategy to implement the Improvement PSO (IPSO) algorithm.Design/methodology/approach - The weighted factor is estimated in the multi-sensor data fusion network. This FLAG Programmable System on Chip (SoC) Developing System 1605A (FLAG-the PSoC-1605A) is an experimental platform, using various types of sensors, combined with ZigBee wireless sensor networks, the TCP / IP network.Findings - The IPSO applies the IOT system in monitoring the environment better than other existing PSO methods in computing precise and convergence rate for excellent fusion results.Originality/value - The experimental results show that the IPSO algorithm optimally integrates the weight factor, information source fusion reliability, information redundancy, complementarity and hierarchical structure integration of an uncertain fusion case. The sensor data approximates the optimal way to extract useful information from each fusion data, successfully eliminates noise interference, and produces excellent fusion results.