The nature of statistical learning theory
The nature of statistical learning theory
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Leave-One-Out Bounds for Support Vector Regression Model Selection
Neural Computation
Gradient-Based Adaptation of General Gaussian Kernels
Neural Computation
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Validation of a water quality model for the Ria de Aveiro lagoon, Portugal
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
Application of chaos and fractal models to water quality time series prediction
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
Artificial neural networks for automated year-round temperature prediction
Computers and Electronics in Agriculture
Grey system theory-based models in time series prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Greenhouse air temperature predictive control using the particle swarm optimisation algorithm
Computers and Electronics in Agriculture
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
A hybrid neural network and ARIMA model for water quality time series prediction
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Computers and Electronics in Agriculture
Original paper: The prediction of seedy grape drying rate using a neural network method
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Electromechanical equipment state forecasting based on genetic algorithm - support vector regression
Expert Systems with Applications: An International Journal
Probabilistic prediction of tunnel geology using a Hybrid Neural-HMM
Engineering Applications of Artificial Intelligence
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
A Cascaded Fuzzy Inference System for Indian river water quality prediction
Advances in Engineering Software
Computers and Electronics in Agriculture
A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
Computers and Electronics in Agriculture
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Assisted management of water exchange in traditional semi-intensive aquaculture ponds
Computers and Electronics in Agriculture
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
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It is important to set up a precise predictive model to obtain clear knowledge of the prospective changing conditions of dissolved oxygen content in intensive aquaculture ponds and to reduce the financial losses of aquaculture. This paper presents a hybrid dissolved oxygen content prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by improved particle swarm optimization (IPSO) algorithm. In view of the slow convergence of particle swarm algorithm (PSO), improved PSO with the dynamically adjusted inertia weight was based on the fitness function value to improve convergence. Then a global optimizer, IPSO, was employed to optimize the hyperparameters needed in the LSSVR model. We adopted an IPSO-LSSVR algorithm to construct a non-linear prediction model. IPSO-LSSVR was tested and compared to other algorithms by applying it to predict dissolved oxygen content in river crab culture ponds. Experiment results show that the proposed model of IPSO-LSSVR could increase the prediction accuracy and execute generalization performance better than the standard support vector regression (SVR) and BP neural network, and it is a suitable and effective method for predicting dissolved oxygen content in intensive aquaculture.