Neuro-Fuzzy GMDH-Based Evolutionary Algorithms to Predict Flow Discharge in Straight Compound Channels
作者： Mohammad Najafzadeh , Abdolreza Zahiri
DOI: 10.1061/(ASCE)HE.1943-5584.0001185
关键词: Nonlinear regression 、 Neuro-fuzzy 、 Group method of data handling 、 Particle swarm optimization 、 Mathematical optimization 、 Algorithm 、 Coherence (signal processing) 、 Communication channel 、 Linear genetic programming 、 Evolutionary algorithm 、 Mathematics 、 Civil and Structural Engineering 、 General Environmental Science 、 Water Science and Technology 、 Environmental chemistry
摘要: AbstractIn this study, neuro-fuzzy-based group method of data handling (NF-GMDH) as an adaptive learning network is used to predict the flow discharge in straight compound channels. The NF-GMDH developed by using particle swarm optimization (PSO) and gravitational search algorithm (GSA). depth ratio (ratio water floodplain that main channel), coherence parameter, [ratio calculated from vertical divided channel (VDCM) bank full discharge] are considered input parameters represent a functional relationship between output parameters. performances training testing stages for models were quantified terms statistical error Also, results compared with those obtained linear genetic programming, nonlinear regression methods, VDCM. Evaluation proposed model demonstrated NF-GMDH-GSA provides more accurate predicti...
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