scholarly journals Artificial Neural Network – Possible Approach to Nonlinear System Control

10.5772/15337 ◽  
2011 ◽  
Author(s):  
Jan Mares ◽  
Petr Dolezel ◽  
Pavel Hrncirik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
System Control ◽  
Nonlinear System Control ◽  
Artificial Neural

scholarly journals An adaptive PID neural network for complex nonlinear system control

2014 ◽  
Vol 135 ◽  
pp. 79-85 ◽  
Author(s):  
Jun Kang ◽  
Wenjun Meng ◽  
Ajith Abraham ◽  
Hongbo Liu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
System Control ◽  
Nonlinear System Control ◽  
Complex Nonlinear System ◽  
Pid Neural Network

A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

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