Unconventional machining process finds lot of application in aerospace and precision industries. It is preferred over other conventional methods because of the advent of composite and high strength to weight ratio materials, complex parts and also because of its high accuracy and precision. Usually in unconventional machine tools, trial and error method is used to fix the values of process para...

Performance of four types of functionally different artificial neural network (ANN) models, namely Feed forward neural network, Elman type recurrent neural network, Input delay neural network and Radial basis function network and fourteen types of algorithms, namely Batch gradient descent (traingd), Batch gradient descent with momentum (traingdm), Adaptive learning rate (traingda), Adaptive lea...

Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y = f(x) that fits the data. This paper describes a system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses a...

An inverse iterative algorithm for microwave imaging based on moment method solution is presented here. A modified Gauss-Newton method has been depicted here to address the nonlinear ill-posed problem. The stabilization term consists of a combination of three weighted discrete derivative operators instead of an Identity matrix as in the Levenberg-Marquardt method based algorithm developed by us...

The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. In this paper, we propose and analyze the global and local convergence results of a novel Levenberg-Marquadt method for solving general nonlinear least squares problems. The proposed algorithm enjoys strong convergence properties (global convergence as well as quadratic ...

Our objective is to solve a parameter identification inverse problem arising in electromagnetics. We give a brief discussion of the methodology used for obtaining forward solutions to Maxwell’s equations in two dimensions, in the presence of a Debye medium. A careful consideration of the noise model for data generation leads to a nonlinear least squares formulation of the inverse problem, and t...

In this paper, a new algorithm for the instantaneous mixture of the blind separation of sources problem is derived. This algorithm deals with Multi-Input Multi-Output (MIMO) channels. The cost function proposed in this paper can be considered as an extension of that previously proposed by us (Mansour-Jutten 6]) for only two sources and two sensors. The cost function is based on the cross-cumula...

Arti3cial neural networks (ANNs) have recently gained attention as fast and 5exible vehicles to microwave modeling, simulation, and optimization. In this study, ANNs, based on the multilayer perceptron, were presented for accurate computation of the quasistatic parameters of asymmetric coplanar waveguides (ACPWs). Multilayer perceptron neural networks (MLPNNs) were trained with backpropagation,...

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...

Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt l...