This work approaches the problem of estimating Kautz optimal poles in kernel expansion Wiener-Volterra models. The analytical solution for suboptimal case is already established literature. However, two parameters that compose still open. In this paper, an optimization strategy using Levenberg-Marquardt presented. algorithm used to find parameters, with same base all dimensions. construction ba...

This document suggests a synthesis of a hybrid observer for switching systems. Such a hybrid observer is made up of a discrete observer identifying the switching signal and another one producing an estimation of the evolution of the continuous state. The developed estimation technique is the moving-horizon-state estimation (MHSE). In fact, this approach enables us to transpose the problem of ob...

The use of cubic splines, instead of polynomials, in representing static nonlinearities in block structured models is considered. A system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, is developed in which the static nonlinearity is represented by a cubic spline. The identification algorithm, based on a separable least squares Levenb...

Selecting appropriate initial values is critical for parameter estimation in nonlinear photosynthetic light response models. Failed convergence often occurs due to wrongly selected initial values when using currently available methods, especially the kind of local optimization. There are no reliable methods that can resolve the conundrum of selecting appropriate initial values. After comparing ...

In this work, a recursive Levenberg-Marquardt (LM) learning algorithm in the complex domain is developed and applied to the learning of an adaptive control scheme composed by ComplexValued Recurrent Neural Networks (CVRNN). We simplified the derivation of the LM learning algorithm using a diagrammatic method to derive the adjoint CVRNN used to obtain the gradient terms. Furthermore, we apply th...

The weld quality of Tunston inert gas welded joint has been investigated to identify the most economical weld parameters that will bring about optimum properties. Artificial neural network, has been used in the prediction and optimization of the Tunston inert gas weld of mild steel pipes. Neural network model was generated using the Levenberg-Marquardt algorithm with feed ward back propagation ...

After development of the theory in the part I paper, systems of up to 300 nonlinear equations are solved in this paper by the Levenberg-Marquardt optimization algorithm. The maximum load is reached when the circumferential cracks begin to form. Numerical calculations show a typical quasi brittle size effect such that the plot of log CYN versus log h (where CXN = nominal stress at maximum load a...

Most previous studies utilized a log-linear regression model to invert multi-spectral images into bathymetric data. Based on the Levenberg-Marquardt optimization algorithm, we developed an automated method for calibrating the parameters for a non-linear inversion model. Our method has been successfully applied to an IKONOS multispectral image. We compared depth data derived from our model to th...

This study presents a new strategy for the identification of material parameters in the case of restricted or redundant data, based on a hybrid approach combining a genetic algorithm and the Levenberg-Marquardt method. The proposed methodology consists essentially in a statistically based topological analysis of the search domain, after this one has been reduced by the analysis of the parameter...