Browsing by Author "Subrahmanyam, P.V."

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    Existence of continuous solutions for an iterative functional series equation with variable coefficients
    (2009) Murugan, V.; Subrahmanyam, P.V.
    We obtain theorems on the existence and uniqueness of the solution for iterative functional equations of the type where Hi's and F are given functions and ?i's are nonnegative functions such that on [a, b]. Stability of the solution is also discussed. Birkh user Verlag, Basel, 2009.
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    Existence of continuous solutions for an iterative functional series equation with variable coefficients
    (2009) Murugan, V.; Subrahmanyam, P.V.
    We obtain theorems on the existence and uniqueness of the solution for iterative functional equations of the type where Hi's and F are given functions and ?i's are nonnegative functions such that on [a, b]. Stability of the solution is also discussed. © Birkhäuser Verlag, Basel, 2009.
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    Koopman Theory Inspired Neural Network for State of Charge Estimation
    (Institute of Electrical and Electronics Engineers Inc., 2024) Gadia, V.; Jaju, A.; Subrahmanyam, P.V.; Jena, D.
    Koopman theory offers a potent framework for mode-by-mode analysis of system dynamics. Inspired by this theory, this work introduces a deep-learning framework utilizing autoencoders and a customized loss function for time series prediction. The work demonstrates the effectiveness of the model through practical application on state of charge (SoC) estimation of a battery. Model based SoC estimation techniques like Extended Kalman Filter(EKF) require complex models and technical data to estimate the SoC of a battery. The proposed model is able to predict the state of charge upto 10 time stamps in the future. This model is able to generalize the system dynamics such that a model trained for T time stamps is seen to give RMSE lesser than 0.01 for all tested temperatures at a future time stamp lesser than T. These findings demonstrate the superior performance of the proposed Koopman-inspired neural network(KoNN) compared to the traditional time series estimation technique EKF, Multivariate Linear Regression(MVLR), Extra Tree Regression(ETR), and Neural Network(NN) showcasing its versatility for various predictive tasks. © 2024 IEEE.