Abstract

The motive of current investigations is to design a computational artificial neural network procedure for the numerical outputs of the fractional order (FO) lumpy skin disease model (LSDM), called as FO-LSDM. The stochastic performances using the optimization of scale conjugate gradient (SCGD) have been implemented to get the solutions of the FO-LSDM. The aim to implement the solutions of the FO is considered more reliable as compared to the integer order. The mathematical form of the LSDM is divided into two populations based on the cattle and vector using the population of susceptible and infected. A numerical Adam scheme is plagued to accomplish the dataset for reducing the mean square error by splitting the statics of endorsement, testing and training as 13%, 12% and 75%. The proposed stochastic neural network approach has a single layer, thirty numbers of neurons, sigmoid activation function, and optimization based SCGD procedure. The exactitude of the SCGD neural network is authenticated through the result comparisons and reducible absolute error around 10-06 to 10-08. Additionally, the correctness of the stochastic process based on the SCGD neural network is evaluated by applying the procedure of state transitions, correlation values, and best training.