The current investigations provide the solutions of nonlinear fractional order mathematical rape and its control model using strength artificial neural networks (ANNs) along with Levenberg-Marquardt backpropagation approach (LMBA), i.e., networks-Levenberg-Marquardt (ANNs-LMBA). have been presented to find more realistic results form model. differential has six classes: susceptible native girls...

By making use of duality mappings and the Bregman distance, we propose a regularizing Levenberg-Marquardt scheme to solve nonlinear inverse problems in Banach spaces, which is an extension of the one proposed in [6] in Hilbert space setting. The method consists of two components: an outer Newton iteration and an inner scheme. The inner scheme involves a family of convex minimization problems in...

The ensemble Kalman smoother (EnKS) is used as a linear least-squares solver in the Gauss–Newton method for the large nonlinear least-squares system in incremental 4DVAR. The ensemble approach is naturally parallel over the ensemble members and no tangent or adjoint operators are needed. Furthermore, adding a regularization term results in replacing the Gauss–Newton method, which may diverge, b...

This paper presents an optimization approach for a system consisting of multiple bidirectional links over a two-way amplify-and-forward relay. It is desired to improve the fairness of the system. All user pairs exchange information over one relay station with multiple antennas. Due to the joint transmission to all users, the users are subject to mutual interference. A mitigation of the interfer...

The Quasi-Linear Geostatistical Approach is a method of inverse modeling to identify parameter fields, such as the hydraulic conductivity in heterogeneous aquifers, given observations of related quantities like hydraulic heads or arrival times of tracers. Derived in the Bayesian framework, it allows to rigorously quantify the uncertainty of the identified parameter field. Since inverse modeling...

The success of an Artificial Neural Network (ANN) strongly depends on its training process. Gradient-based techniques have been satisfactorily used in the ANN training. However, in many cases, these algorithms are very slow and susceptible to the local minimum problem. In our work, we implemented a hybrid learning algorithm that integrates Genetic Algorithms(GAs) and the LevenbergMarquardt(LM) ...