The inspiration to exploit the Morlet wavelet neural network process and the hybridization methods comes to present precise and reliable solutions for solving the nonlinear and stiff differential systems.
Abstract
The present work shows the numerical performances through stochastic paradigms of the reactive transport system (RTS). The designed construction of the solver is presented through the Morlet wavelet function, which works as an artificial neural network and the hybridization of genetic algorithm and active set. An objective function is created using the differential RTS, which is optimized through the hybrid capability of the designed scheme. The precision of the solver is perceived in comparison with the obtained and reference outcomes, absolute error, and number of multiple executions based on different operators. The inspiration to exploit the Morlet wavelet neural network process and the hybridization methods comes to present precise and reliable solutions for solving the nonlinear and stiff differential systems. Three RTS cases are numerically treated to validate the efficiency of the designed process, and the performance of the scheme is also validated by using different statistical operators.
In this study the conclusion is drawn that a neural network should not be used to replace traditional methods, but should be implemented alongside, to complement.
Oday Hatem Jalel· International journal of res...· 0 citations
Three neural networks are compared as solvers of nonlinear ordinary differential equations: a multilayer perceptron (MLP), a Kolmogorov-Arnold network (KAN) whose edges are B-splines, and a KAN whose edges are Chebyshev series. Each network is trained only on the residual of the equation and on its initial or boundary...
Mahidhar Reddy Patukuri, B. V. S. N. Rao, S. Mallikarjunaiah· 0 citations
This paper presents a comparative study of classical numerical and machine learning techniques for solving the nonlinear Monge–Ampère Equation (MAE). A finite difference method (FDM) implemented in MATLAB is first validated using a quadratic exact solution on a square domain. Two neural network approaches are then inve...
C. Ogbonna, Maliki Olaniyi S, Nkem Ogbonna· International journal of re...· 0 citations
Recently, fractional optimal control problems (FOCPs) have attracted significant attention due to their ability to describe dynamical systems. In this study, an efficient computational framework is proposed based on a novel class of wavelet functions, called Horadam wavelets (HWs), to solve FOCPs involving Caputo‐typ...
Zahra Najafi, Y. Ordokhani, S. Sabermahani· Optimal control applications...· 0 citations
In this pepear we develop a type of high-performance algorithm called a Quasi–Newton type training algorithm by designing a partial fuzzy neural network with fuzzy weights using the $\omega$-cut representation, and real biases (PFNN).Developing feed forward neural networks (FFNNs) to solve fuzzy singular perturbed Fre...
K. M. M. Al-Abrahemee, Thaer Ubaid Al-Jubouri· Boletim da Sociedade Paranae...· 0 citations
This paper applied a circuit implemented ReLU Hopfield Neural Network to a mathematical problem with an inequality constraint and implemented a circuit and observed converged neural circuit outputs that corresponded well to theoretical results and simulations.
Ryosei Okubo, Y. Mitsuya, Hiroyuki Takahashi· Frontiers in Neuroscience· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
