Inverse problems are typically ill-posed or ill-conditioned and require regularization. Tikhonov regularization is a popular approach and it requires an additional parameter called the regularization parameter that has to be estimated. The χ method introduced by Mead in [8] uses the χ distribution of the Tikhonov functional for linear inverse problems to estimate the regularization parameter. H...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...

The resection in 3D space is a common problem surveying engineering and photogrammetry based on observed distances, angles, coordinates. This nonlinear comprises redundant observations which normally solved using the least-squares method an iterative approach. In this paper, we introduce vigorous angular that converges to global minimum even with very challenging starting values of unknowns. de...

Artificial neural networks (ANNs) have been promising tools for many applications. In recent years, a computer-aided design approach based on (ANNs) has been introduced to microwave modelling, simulation and optimization. In this work, the characteristic parameters of top shielded multilayered coplanar waveguides (CPWs) have been determined with the use of ANN models. These neural models were t...

In this paper, we propose an artificial neural network (ANN) as a design technique for multilayer circular microstrip antennas based on Levenberg Marquardt training algorithm for modelling, simulation and optimization. Levenberg – Marquart (LM) algorithm has been used to train the MultiLayer Perceptron Neural Networks (MLPNNs). In the design procedure, the feed forward network is defined as a s...

In this paper we develop a convergence analysis in an infinite dimensional setting of the Levenberg–Marquardt iteration for the solution of a hybrid conductivity imaging problem. The problem consists in determining the spatially varying conductivity from a series of measurements of power densities for various voltage inductions. Although this problem has been very well studied in the literature...

In this work, we describe a method for the classification of respiratory states based on four significant features using Artificial neural network (ANN). These features are extracted from the respiratory signals using modified threshold algorithm were fed as input parameters to the ANN for classification. A gradient based search algorithms are usually being used in ANN to find a set of suitable...

We propose a new optimizer in the context of multimodal image registration. The optimized criterion is the mutual information between the images to align. This criterion requires that their joint histogram be available. For its computation, we introduce differentiable and separable Parzen windows that satisfy the partition of unity. Along with a continuous model of the images based on splines, ...

In this paper, a Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which ...