Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

• Published in: Engineering with computers Vol. 37; no. 1; pp. 265 - 274
• Main Authors: Zhou, Jian, Li, Chuanqi, Arslan, Chelang A., Hasanipanah, Mahdi, Bakhshandeh Amnieh, Hassan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2021; Springer Nature B.V
• Subjects: Adaptive systems; Artificial neural networks; Blasting; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computer Science; Computer-Aided Engineering (CAD; Control; Fragmentation; Fuzzy logic; Fuzzy systems; Genetic algorithms; Heuristic methods; Math. Applications in Chemistry; Mathematical and Computational Engineering; Mining industry; Model accuracy; Original Article; Particle size; Particle size distribution; Performance evaluation; Prediction models; Root-mean-square errors; Support vector machines; Systems Theory; ANFIS; Blasting; Genetic algorithm; Firefly algorithm; Rock fragmentation

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and conseq...