Title :
Process identification using Hammerstein model with gradient descent algorithm
Author :
Chakraborty, Arpan ; Patra, Sankar Narayan
Author_Institution :
Appl. Electron. & Instrum. Dept., Univ. Inst. of Technol., Burdwan, India
fDate :
Jan. 31 2014-Feb. 2 2014
Abstract :
The paper demonstrates a method for process optimization with the help of a Hammerstein Model hybridized with Gradient Descent Algorithm (for the minimization of error). This involves determination of the local minimum of error function and thus provides the corrective adjustments to the Hammerstein model parameters for the foresaid optimization.
Keywords :
gradient methods; identification; minimisation; nonlinear systems; Hammerstein model; error minimization; gradient descent algorithm; process identification; process optimization; Algorithm design and analysis; Biological system modeling; Convergence; Instruments; Iterative methods; Mathematical model; Optimization; Artificial Neural Network (ANN); Gradient Descent Algorithm (GDA); Hammerstein Model; LTI System; Process Identification;
Conference_Titel :
Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on
Conference_Location :
Calcutta
DOI :
10.1109/CIEC.2014.6959188
