Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Improved Particle Swarm Optimization Based Adaptive Neuro‐Fuzzy Inference System for Benzene Detection
Benzene is a carcinogen and employing hardware sensors to detect its concentration is expensive, along with limited operational efficiency. There is a relation among various atmospheric gas concentrations and therefore, some heuristic regression approaches can be applied for benzene forecasting, if given the concentration level of other gases. This paper proposes a new adaptive benzene prediction model using an improved particle swarm optimization (PSO) based adaptive neuro fuzzy inference system (ANFIS). Improved PSO enhances the performance of ANFIS by considering the multi‐objective fitness function involving accuracy, root mean squared error (RMSE), and coefficient of determination (r2). The proposed technique has been tested on both publicly available air quality datasets and a real world dataset of Patiala City in India. Extensive analysis reveals that the proposed technique outperforms other state‐of‐the‐art techniques, making it well suited for building effective and economical benzene prediction models.
Improved Particle Swarm Optimization Based Adaptive Neuro‐Fuzzy Inference System for Benzene Detection
Benzene is a carcinogen and employing hardware sensors to detect its concentration is expensive, along with limited operational efficiency. There is a relation among various atmospheric gas concentrations and therefore, some heuristic regression approaches can be applied for benzene forecasting, if given the concentration level of other gases. This paper proposes a new adaptive benzene prediction model using an improved particle swarm optimization (PSO) based adaptive neuro fuzzy inference system (ANFIS). Improved PSO enhances the performance of ANFIS by considering the multi‐objective fitness function involving accuracy, root mean squared error (RMSE), and coefficient of determination (r2). The proposed technique has been tested on both publicly available air quality datasets and a real world dataset of Patiala City in India. Extensive analysis reveals that the proposed technique outperforms other state‐of‐the‐art techniques, making it well suited for building effective and economical benzene prediction models.
Improved Particle Swarm Optimization Based Adaptive Neuro‐Fuzzy Inference System for Benzene Detection
Pannu, Husanbir S. (Autor:in) / Singh, Dilbag (Autor:in) / Malhi, Avleen K. (Autor:in)
01.05.2018
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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