A numerical simulation of the fractional order Leptospirosis model using the supervise neural network

Abstract

The aim of this work is to present the numerical simulations of the novel designed fractional order Leptospirosis model (FOLM) by using the strength of stochastic numerical supervised neural networks. This novel work provides the numerical study of the Leptospirosis model, which is classified into five dynamics. Different values of the fractional order derivatives have been provided to solve the biological FOLM. The numerical formulations of the FOLM are obtained through the supervised neural networks (SNNs) along with the computational performances of the Levenberg-Marquardt backpropagation (LVMBP), i.e., SNNs-LVMBP. The correctness of the procedure is observed by using the comparative performances of the obtained and reference solutions. The statics are performed for these investigations as 74% and 13% for both certification and learning. The process of error histograms (EHs), recurrence, MSE, correlation, and state transitions (STs) will be performed to authenticate the capability, steadiness, accuracy, reliability, and fitness of the proposed procedure.

Keywords

• Biological system
• Fractional order Leptospirosis model
• Supervised neural networks
• Numerical results
• Levenberg-Marquardt backpropagation