Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model

In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) by applying the global approximation capability of a genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous and historical PDM is known as a variant of the functional differential system that works as theopposite of the delay differential models. A fitness function is constructed by using the mean square error and optimized through the GA-IPAS for solving the PDM. Three PDM examples have been presented numerically to check the authenticity of the MWNN-GAIPAS. For the perfection of the designed MWNN-GAIPAS, the comparability of the obtained outputs and exact results is performed. Moreover, the neuron analysis is performed by taking 3, 10, and 20 neurons. The statistical observations have been performed to authenticate the reliability of the MWNN-GAIPAS for solving the PDM.

Automated summary

In this study, a design of Morlet wavelet neural networks (MWNNs) is presented, which applies the global approximation capability of a genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS) to solve the prediction differential model (PDM). Several numerical examples and statistical observations are conducted to verify the authenticity and reliability of MWNN-GAIPAS for solving PDM.