Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

作     者：Maria del Carmen Cabrera-Hernandez Marco Antonio Aceves-Fernandez Juan Manuel Ramos-Arreguin Jose Emilio Vargas-Soto Efren Gorrostieta-Hurtado 

作者机构：Faculty of Engineering Universidad Autónoma de Querétaro Cerro de las Campanas S/N Querétaro Mexico 

出 版 物：《International Journal of Intelligence Science》 (智能科学国际期刊（英文）)

年 卷 期：2019年第9卷第3期

页      面：67-91页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Air Pollution Bacterial Foraging Optimization Algorithm (BFOA) Swarm Intelligence ANFIS 

摘      要：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.