MODELLING OF NONLINEAR SYSTEMS USING INTEGRATED OBFARX PLUS NEURAL NETWORK MODELS

The objective of this project is to develop a new model, which is by combining OBFARX linear model with nonlinear NN model. The results obtained will be compared with the previous models to show performance improvement by the new model. The new model development is based on the model developed by (Zabiri et al 2011) which is OBF linear model combination with nonlinear NN model. The OBF-NN model cannot work efficiently on some problems due to the limitations of the OBF part of the equation. So it is important to analyze the new model which is OBFARX-NN with OBF-NN model. The scope for this project will be the development of the parallel OBFARX-NN model, methods for estimating the model parameter, simulation analysis using MATLAB and evaluation on OBFARX-NN model performance. The method for completing the project will be firstly, make sure all the necessary information about the individual model is available. Then develop a theoretically working OBFARX-NN model. After that, analysis of the performance of the created model is done and also alterations here and there for better clarification. All in all, the result are the improve performance of process control by OBFARX-NN model compared to OBF-NN model.The most important aspect of the model development is the extrapolation capabilities of the model itself. When a model is forced to perform prediction in regions beyond the space of original training, then it can be said that the model can function well even when the process parameter is changed. This aspect is very important because in practical plant, the process conditions are continually changing making extrapolation inevitable. Thus, by testing the extrapolation capabilities of the OBFARX-NN model, the project had come up with the subsequent RMSE value and compared with previous model. The RMSE value indicates superior performance in the extrapolation region

MODELLING OF NONLINEAR SYSTEMS USING INTEGRATED OBFARX PLUS NEURAL NETWORK MODELS

The objective of this project is to develop a new model, which is by combining OBFARX linear model with nonlinear NN model. The results obtained will be compared with the previous models to show performance improvement by the new model. The new model development is based on the model developed by (Zabiri et al 2011) which is OBF linear model combination with nonlinear NN model. The OBF-NN model cannot work efficiently on some problems due to the limitations of the OBF part of the equation. So it is important to analyze the new model which is OBFARX-NN with OBF-NN model. The scope for this project will be the development of the parallel OBFARX-NN model, methods for estimating the model parameter, simulation analysis using MATLAB and evaluation on OBFARX-NN model performance. The method for completing the project will be firstly, make sure all the necessary information about the individual model is available. Then develop a theoretically working OBFARX-NN model. After that, analysis of the performance of the created model is done and also alterations here and there for better clarification. All in all, the result are the improve performance of process control by OBFARX-NN model compared to OBF-NN model.The most important aspect of the model development is the extrapolation capabilities of the model itself. When a model is forced to perform prediction in regions beyond the space of original training, then it can be said that the model can function well even when the process parameter is changed. This aspect is very important because in practical plant, the process conditions are continually changing making extrapolation inevitable. Thus, by testing the extrapolation capabilities of the OBFARX-NN model, the project had come up with the subsequent RMSE value and compared with previous model. The RMSE value indicates superior performance in the extrapolation region

Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of “knowledge” and “data”, and coupling between the greenhouse system elements have been considered as future valuable development

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Solar power satellite. Concept evaluation. Activities report. Volume 2: Detailed report

Comparative data are presented among various design approaches to thermal engine and photovoltaic SPS (Solar Power System) concepts, to provide criteria for selecting the most promising systems for more detailed definition. The major areas of the SPS system to be examined include solar cells, microwave power transmission, transportation, structure, rectenna, energy payback, resources, and environmental issues