In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant...

this paper describes a study of operational parameters by using the multivariate data analysis and neural networks for a municipal waste incinerator located in majorca (spain). the basis of the study also includes the chemometric techniques: linear multivariate regression to develop a model with certain predictive capabilities; linear principal component analysis, which allow the number of vari...

the safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. in this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (hdpe), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. t...

1. Abstract The objective of this paper is to report the results from the research being conducted in reconfigurable flight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft’s control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive ...

This paper focuses on the developing a mechanistic model of the biopolymerization process and linking with the feedforward neural network model to obtain a hybrid model of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) PCL production using feed forward neural networks and control using model predictive control. Model predictive control (MPC) has ...