Paper Info
Title
Water Temperature Forecasting in Sea Cucumber Aquaculture Ponds by RBF Neural Network Model.
Abstract
Water temperature is considered to be the most important parameter which can largely determine the aquaculture production of sea cucumbers, so it is extremely important to monitor and forecast the water temperature at different water depths. As the change of water temperature is a complex process which can not be exactly described with a certain formula, the artificial neural network characterized by non-linearity, adaptivity, generalization, and model independence is a proper choice. This paper presents a RBF neural network model based on nearest neighbor clustering algorithm and puts forward four improved methods, then integrates them into an optimization model and verifies it on matlab platform. Finally, a comparison between the optimized RBF model and the original RBF model is made to confirm the excellent forecasting performance of the optimized RBF neural network model. This paper provides a relatively impeccable learning algorithm to complete the choice of radial basis clustering center in the process of RBF network design, and obtains a high forecasting precision so that the demand of water temperature forecasting in sea cucumber aquaculture ponds can be satisfied. © 2013 IFIP International Federation for Information Processing.
Year
DOI
Venue
2012
10.1007/978-3-642-36124-1_51
IFIP Advances in Information and Communication Technology
Keywords
Field
DocType
nearest neighbor clustering algorithm,RBF neural network,sea cucumber,water temperature
k-nearest neighbors algorithm,Aquaculture,MATLAB,Network planning and design,Computer science,Artificial intelligence,Water temperature,Cluster analysis,Artificial neural network
Conference
Volume
Issue
ISSN
392 AICT
PART 1
null
Citations 
PageRank 
References 
0
0.34
8
Authors
6
Name
Order
Citations
PageRank
Shuangyin Liu1305.89
Longqin Xu2344.64
Ji Chen300.34
Daoliang Li433481.09
Haijiang Tai5173.28
Lihua Zeng632.83