A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

Authors:
Qi Wu
Affiliations:
School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu 210096, China and Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry o ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 16
Cited 18

The nature of statistical learning theory

The nature of statistical learning theory
An asymptotic statistical analysis of support vector machines with soft margins

Neural Networks
Information gain and divergence-based feature selection for machine learning-based text categorization

Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
2008 Special Issue: Improved mapping of information distribution across the cortical surface with the support vector machine

Neural Networks
Particle swarm optimization for parameter determination and feature selection of support vector machines

Expert Systems with Applications: An International Journal
BP neural network with rough set for short term load forecasting

Expert Systems with Applications: An International Journal
Optimal tunning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization

Expert Systems with Applications: An International Journal
Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM

Expert Systems with Applications: An International Journal
Applying particle swarm optimization algorithm to roundness measurement

Expert Systems with Applications: An International Journal
Forecasting volatility based on wavelet support vector machine

Expert Systems with Applications: An International Journal
Support vector machines combined with feature selection for breast cancer diagnosis

Expert Systems with Applications: An International Journal
Cross-correlation aided support vector machine classifier for classification of EEG signals

Expert Systems with Applications: An International Journal
A Hybrid Forecasting Model Based on Chaotic Mapping and Improved v-Support Vector Machine

ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method

Expert Systems with Applications: An International Journal
The forecasting model based on wavelet ν-support vector machine

Expert Systems with Applications: An International Journal
Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM

Expert Systems with Applications: An International Journal

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

Journal of Computational and Applied Mathematics
Car assembly line fault diagnosis based on robust wavelet SVC and PSO

Expert Systems with Applications: An International Journal
Fault diagnosis model based on Gaussian support vector classifier machine

Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on modified support vector classifier machine

Expert Systems with Applications: An International Journal
Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space

Expert Systems with Applications: An International Journal
Fuzzy robust ν-support vector machine with penalizing hybrid noises on symmetric triangular fuzzy number space

Expert Systems with Applications: An International Journal
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise

Expert Systems with Applications: An International Journal
Hybrid fuzzy support vector classifier machine and modified genetic algorithm for automatic car assembly fault diagnosis

Expert Systems with Applications: An International Journal
Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

Expert Systems with Applications: An International Journal
Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach

Expert Systems with Applications: An International Journal
A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong

Expert Systems with Applications: An International Journal
Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

Expert Systems with Applications: An International Journal
The complex fuzzy system forecasting model based on triangular fuzzy robust wavelet υ-support vector machine

Expert Systems with Applications: An International Journal
Hybrid wavelet υ-support vector machine and chaotic particle swarm optimization for regression estimation

Expert Systems with Applications: An International Journal
Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning

Computers & Mathematics with Applications
Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm

Neurocomputing
Implementing support vector regression with differential evolution to forecast motherboard shipments

Expert Systems with Applications: An International Journal
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	12.07

Visualization

Abstract

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. This paper aims at the Gaussian noise parts of load series the standard v-support vector regression machine with @e-insensitive loss function that cannot deal with it effectively. The relation between Gaussian noises and loss function is built up. On this basis, a new v-support vector machine (v-SVM) with the Gaussian loss function technique named by g-SVM is proposed. To seek the optimal unknown parameters of g-SVM, a chaotic particle swarm optimization is also proposed. And then, a hybrid-load-forecasting model based on g-SVM and embedded chaotic particle swarm optimization (ECPSO) is put forward. The results of application of load forecasting indicate that the hybrid model is effective and feasible.