Selective combination of multiple neural networks for improving model prediction in nonlinear systems modelling through forward selection and backward elimination

2009 ◽  
Vol 72 (4-6) ◽  
pp. 1198-1204 ◽  
Author(s):  
Zainal Ahmad ◽  
Jie Zhang
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Model Prediction ◽  
Forward Selection ◽  
Backward Elimination ◽  
Multiple Neural Networks ◽  
Systems Modelling

Simple adaptive control for SISO nonlinear systems using multiple neural networks

2007 ◽  
Author(s):  
Muhammad Yasser ◽  
Agus Trisanto ◽  
Ayman Haggag ◽  
Takashi Yahagi ◽  
Hiroo Sekiya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Multiple Neural Networks

Intelligent control for Hammerstein nonlinear systems with arbitrary deadzone input

2015 ◽  
Vol 39 (4) ◽  
pp. 567-578 ◽  
Author(s):  
Bi Zhang ◽  
Zhizhong Mao ◽  
Tingfeng Zhang
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Intelligent Control ◽  
Adaptive Controller ◽  
Control Input ◽  
Adaptive Controllers ◽  
Multiple Neural Networks ◽  
Control Scheme ◽  
Robust Adaptive ◽  
Robust Adaptive Controller

In this paper, a new intelligent control scheme based on multiple models and neural networks is proposed to adaptively control a class of Hammerstein nonlinear systems with arbitrary deadzone input. This approach consists of a linear robust adaptive controller, multiple neural networks-based nonlinear adaptive controllers and a switching mechanism. Since the control input is derived from a modified certainty equivalent principle, the manner in which the closed-loop stability is established forms the main contribution. To show the usefulness of the developed results, three simulation examples, including a direct current motor subject to a nonlinear friction, are studied.

scholarly journals AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

2017 ◽  
Vol 18 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Zainal Ahmad ◽  
Nazira Anisa Rahim ◽  
Alireza Bahadori ◽  
Jie Zhang
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Mean Squared Error ◽  
Pollution Index ◽  
Forward Selection ◽  
Combination Methods ◽  
Multiple Neural Networks ◽  
Monitoring And Forecasting ◽  
Precautionary Measures ◽  
And Performance

Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN) is shown to be able to predict the Air Pollution Index (API) with a Mean Squared Error (MSE) and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN) is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

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