Recurrent Neural Network Based Modelling of Industrial Grinding Time Series Data

Inapakurthi, Ravi Kumar and Miriyala, S S and Mitra, Kishalay (2019) Recurrent Neural Network Based Modelling of Industrial Grinding Time Series Data. In: Sixth Indian Control Conference (ICC), 18-20 December 2019, Hyderabad, India.

Full text not available from this repository. (Request a copy)


Modelling the time series data generated from a complex and nonlinear industrial grinding unit mandates the use of sophisticated algorithms capable of efficiently approximating the system under consideration. Recurrent Neural Networks, which are proven to be competent enough to approximate many time series systems, can be utilized for identification of industrial grinding circuits. However, the usage of RNNs for system identification tool is limited due to the heuristic estimation of network hyper parameters viz., number of hidden layers to be explored, number of nodes in each hidden layer, activation function and number of previous time instances to be considered for capturing the dynamics of the process. In this study, we address this heuristic approach by proposing an algorithm which can determine the optimal values of these hyper parameters for RNNs. This optimal determination of hyper parameters is done by adopting a multi-objective optimization problem with maximization of the accuracy of the developed model and minimization of the number of nodes in the network as the two conflicting objectives. The performance of the proposed algorithm on a real life industrial grinding circuit data shows its success and competitiveness.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalay
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks, Identification, Machine learning
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 17 Dec 2019 06:31
Last Modified: 17 Dec 2019 06:31
Publisher URL:
Related URLs:

    Actions (login required)

    View Item View Item
    Statistics for RAIITH ePrint 7169 Statistics for this ePrint Item