Bio-inspired heuristics for layer thickness optimization in multilayer piezoelectric transducer for broadband structures

Zameer, Aneela; Majeed, Mohsin; Mirza, Sikander M.; Raja, Muhammad Asif Zahoor; Khan, Asifullah; Mirza, Nasir M.

doi:10.1007/s00500-017-3002-z

Cited by 38 publications

(13 citation statements)

References 36 publications

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“…In order to get better performance of statistical methods, machine learning (ML) models which cover several fields such as power and energy engineering [14] , technology [15] , psychology [16] , is used for early prediction and real-time spread of data. Recently, one of ML approach namely, infection size aware random forest (iSARF), observed by classification group has been proposed, which highlights the infection size and lung fields [17] .…”

Section: Introductionmentioning

confidence: 99%

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Shahid

Zameer

Muneeb

2020

Chaos, Solitons & Fractals

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COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.

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Section: Introductionmentioning

confidence: 99%

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Shahid

Zameer

Muneeb

2020

Chaos, Solitons & Fractals

Self Cite

588

300

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“…In GAs, the population of individual frequently changes/updated, i.e., applicant solutions of optimization assignment, and have the capability to solve various optimization models by unifying its reproduction tools through the operators; crossover, selection, elitism, and mutation. Some recent applications exploited by GAs are optimization of variables in nonlinear Bratu system (Hassan et al 2019), layer thickness optimization for the multilayer piezoelectric transducer (Zameer 2019), integrated load dispatch problems involving both thermal and wind generators (Jamal 2019), estimation of parameters in electromagnetic plane waves (Akbar 2017), and analysis of the dynamics for heartbeat model (Raja et al 2017b). The sluggishness and laziness of GAs through hybridization with the local search algorithms can be upgraded and improved during the process of optimization.…”

Section: Network Optimizationmentioning

confidence: 99%

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

et al. 2020

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In the present study, a novel fractional Meyer neuro-evolution-based intelligent computing solver (FMNEICS) is presented for numerical treatment of doubly singular multi-fractional Lane-Emden system (DSMF-LES) using combined heuristics of Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The design of novel FMNE-ICS for DSMF-LES is presented after derivation from standard Lane-Emden equation, and the singular points and shape factors along with fractional-order terms are analyzed. The MWNN modeling strength is used to represent the system model DSMF-LES in the meansquared error-based merit function and optimization of the networks is carried out with integrated optimization ability of GASQP. The verification, validation, and perfection of the FMNEICS for three different cases of DSMF-LES are established through comparative studies from reference solutions on convergence, robustness, accuracy, and stability measures. Moreover, the observations through the statistical analysis further authenticate the worth of proposed fractional MWNN-GASQP-based stochastic solver. Keywords Multi-fractional Lane-Emden model • Multi-singular systems • Artificial neural networks • Meyer wavelet neural networks • Sequential quadratic programming • Genetic algorithms Mathematics Subject Classification 34-XX • 34A08 • 34A34 • 82B31

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“…The algorithm is expertly used for convex/non-convex and constrained/unconstrained optimization problems having application in engineering and technology [51][52][53][54]. Few recently reported significance application includes are joint angle-amplitude estimation in direction of arrival of the plane waves [55], effective solution of traveling salesman problem [56], layer thickness optimization in multilayer piezoelectric transducer based systems [57], prediction of Thai stock price index trend [58] and nonlinear fuzzy systems [59] etc.…”

Section: Learning Of Weights For Nndemsmentioning

confidence: 99%

Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

Mehmood

Zameer

Ling

et al. 2019

Neural Comput & Applic

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In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of Genetic algorithms (GA), and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adams numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory.

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Bio-inspired heuristics for layer thickness optimization in multilayer piezoelectric transducer for broadband structures

Cited by 38 publications

References 36 publications

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

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