Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning

Abstract

In this article application of neural network using Levenberg–Marquardt Back-propagation is implemented on differential model to study and analyze ringworm infectious disease. The formulated system of differential equations is consisting of following parts, namely; S(t) population which is at verge of being infected by ringworm, E(t) shows the environment effected by dermetophytosis fungus, I(t) whereas represent the infected individuals, R(t) and shows the population which has recovered from the infection. The solutions of different categories are represented by considering distinct datasets modeled and designed using LMB neural network. The numerical scheme Adam has been employed to establish a reference data set of the designed LMB neural network. The approximate outcomes of the SEIR based on dispersing and curing are discussed using the authentication, testing and training procedures to truncate the mean square error in function with help of LMB. The mean square error, regression, error histograms are generated to produce efficiency, effectiveness and correctness of proposed LMB neural network scheme.

Keywords

• Biological model
• Ringworm infection
• Artificial neural networks
• Reference results
• Levenberg–Marquardt back-propagation