The
airborne pollutants monitoring is an overriding task for humanity given that
poor quality of air is a matter of public health, causing issues mainly in the
respiratory and cardiovascular systems, specifically the PM10 particle. In this
contribution is generated a base model with an Adaptive Neuro Fuzzy Inference
System (ANFIS) which is later optimized, using a swarm intelligence technique,
named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were
carried with BFOA parameters, tuning them to achieve the best configuration of
said parameters that produce an optimized model, demonstrating that way, how
the optimization process is influenced by choice of the parameters.
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