Design of Morlet wavelet neural network to solve the non-linear influenza disease system

Abstract

In this study, the solution of the non-linear influenza disease system (NIDS) is presented using the Morlet wavelet neural networks (MWNNs) together with the optimisation procedures of the hybrid process of global/local search approaches. The genetic algorithm (GA) and sequential quadratic programming (SQP), that is, GA-SQP, are executed as the global and local search techniques. The mathematical form of the NIDS depends upon four groups: susceptible S(y), infected I(y), recovered R(y) and cross-immune individuals C(y). To solve the NIDS, an error function is designed using NIDS and its leading initial conditions (ICs). This error function is optimised with a combination of MWNNs and GA-SQP to solve for all the groups of NIDS. The comparison of the obtained solutions and Runge–Kutta results is presented to authenticate the correctness of the designed MWNNs along with the GA-SQP for solving NIDS. Moreover, the statistical operators using different measures are presented to check the reliability and constancy of the MWNNs along with the GA-SQP to solve the NIDS.

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

“…In the future, one can exploit/explore the knacks of ANNs-PSO-IPM to solve the singular higher order models (Sabir et al 2020c, d;2021a), fractional order models (I ˙lhan and Kıymaz 2020; Sabir et al 2022b;Sulaiman et al 2019;Touchent et al 2020;Yokus ¸and Gu ¨lbahar 2019;Ziane et al 2019) and many other applications of utmost importance (Kouider and Polat 2020;Xie et al 2020;Xue et al 2021;Yao 2021).…”

Dynamical investigation and FPGA implementation of a new Heartbeat model based on the Barrio-Varea-Aragon-Maini oscillator

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