Author
Priyaranjan Mandal
Other affiliations: JIS College of Engineering
Bio: Priyaranjan Mandal is an academic researcher from University of Calcutta. The author has contributed to research in topics: Probabilistic neural network & Recurrent neural network. The author has an hindex of 2, co-authored 4 publications receiving 25 citations. Previous affiliations of Priyaranjan Mandal include JIS College of Engineering.
Papers
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TL;DR: A new set of hybrid functions (HF) which evolved from the synthesis of sample-and-hold functions (SHF) and triangular functions (TF) is proposed which is employed for solving identification problem from impulse response data.
23 citations
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TL;DR: This paper characterizes oscillations found in block pulse function (BPF) domain identification of open loop first-order systems with step input by presenting a useful condition for occurrence of such oscillations.
Abstract: This paper characterizes oscillations found in block pulse function (BPF) domain identification of open loop first-order systems with step input. A useful condition for occurrence of such oscillations is presented mathematically. For any positive value of ‘ah’, oscillations are observed to occur, where h is the width of BPF domain sub-interval and 1 / a is the time constant of the first-order system under consideration.
3 citations
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01 May 2014TL;DR: In this paper, a MATLAB simulink model of an alternator is considered to be connected to an infinite bus and a neural network based approach is applied to study the transient stability of the same system.
Abstract: This paper uses the neural network theory to study the transient stability. The MATLAB simulink model of an alternator is considered to be connected to an infinite bus. The transient stability of this alternator is studied after a phase fault. The relative rotor angle is taken as the measuring parameter. A neural network based approach is applied to study the transient stability of the same system. This approach gives expected results.
2 citations
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01 Jan 2014TL;DR: A neural network (NN) controller is approximated by method of `training', using a reference model, to identify a plant by learning the behaviour of a given nonlinear plant.
Abstract: In this paper, a neural network (NN) controller is approximated by method of ‘training’, using a reference model, to identify a plant. At first, the plant identification neural network, is realized by learning the behaviour of a given nonlinear plant. The outputs of this neural network and the actual plant are compared. The knowledge of the comparison is fed back to the NN controller as its input. The NN controller is realized in such a way that it will control the plant behaviour as a reference.
Cited by
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TL;DR: Simulation results show that the obtained models can capture the dynamics of the systems, i.e., the estimated model's outputs are close to the outputs of the actual systems.
153 citations
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01 May 2011
18 citations
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TL;DR: The proposed numerical technique is based on newly computed generalized triangular function operational matrices for Riemann-Liouville fractional order integral, which encourages the use of orthogonal TFs for analysis of real processes exhibiting fractional dynamics.
16 citations
01 Jan 2015
TL;DR: In this paper, the generalized triangular function operational matrices for approximating Riemann-Liouville fractional order integral in the triangular function (TF) domain are derived.
Abstract: This article introduces a new application of piecewise linear orthogonal triangular functions to solve fractional order differential-algebraic equations. The generalized triangular function operational matrices for approximating Riemann-Liouville fractional order integral in the triangular function (TF) domain are derived. Error analysis is carried out to estimate the upper bound of absolute error between the exact Riemann-Liouville fractional order integral and its TF approximation. Using the proposed generalized operational matrices, linear and nonlinear fractional order differential-algebraic equations are solved. The results show that the TF estimate of Riemann-Liouville fractional order integral is accurate and effective.
8 citations
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TL;DR: The present study proposes a new approach for designing stable adaptive fractional-order proportional-integral-derivative (FOPID) controllers, which employs non-sinusoidal orthogonal function (NSOF) domain-based design approach, which simplifies and eliminates the complexity of solving fractiona-order system dynamics.
Abstract: The present study proposes a new approach for designing stable adaptive fractional-order proportional-integral-derivative (FOPID) controllers, which employs non-sinusoidal orthogonal function (NSOF) domain-based design approach. The objective is to design a self-adaptive FOPID controller such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory transient performance. The proposed design methodology simplifies and eliminates the complexity of solving fractional-order system dynamics by converting it into the algebraic vector-matrix equation with the help of NSOF. The conventional FOPID, NSOF-based FOPID and NSOF-based adaptive FOPID controllers are implemented for benchmark simulation case studies and real-life experimentation and their results demonstrate the usefulness of the proposed approach.
7 citations