Researchers from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS) have proposed an improved algorithm called Dynamic Quantum Particle Swarm Optimization (DQPSO) to improve the accuracy and reliability of pressure sensors used in tracking and monitoring wild migratory birds. This algorithm optimizes the performance of a Radial Basis Function (RBF) neural network, specifically designed for temperature compensation.
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Source: Phys.org