Abstract
The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.
Highlights
In the dredging process of the dredger, the dredging output is mainly caused by the excavation of the rake head
The Extreme Learning Machine (ELM) algorithm only needs to set the number of hidden layer neurons
During the dredging construction of the suction dredger, the factors affecting the dredging output can be divided into the excavation volume of the rake head and the overflow of the mud hopper
Summary
In the dredging process of the dredger , the dredging output is mainly caused by the excavation of the rake head. In view of the research on the production of rake head, this paper uses the extreme learning machine (ELM) algorithm to analyze the data black box of rake head. The Extreme Learning Machine (ELM) algorithm only needs to set the number of hidden layer neurons It can obtain the unique optimal solution without adjusting the neuron weights and thresholds. In order to improve the generalization performance of the Extreme Learning Machine (ELM) model, some literatures use particle swarm optimization algorithm [10] and genetic algorithm [11] to optimize the connection weight and hidden layer threshold of ELM. The bat algorithm is used to optimize the extreme learning machine (ELM) This method is easy to realize the dynamic conversion between the global search and the local search. Compared with other optimization algorithms, the structure is simple, the parameter setting is less, and the method is better Convergence performance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
