Two well known optimization algorithms, the Genetic Algorithm (GA) and the Simulated Annealing Algorithm (SAA), are investigated for IIR adaptive phase equalizers. For non-convex error surfaces, gradient-based algorithms often fail to find the global optimum. This work compares the ability of the GA and the SAA to achieve the global minimum solution for multi-order all-pass adaptive filters to ...

The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of nonlinear conjugate gradient method with strong convergence for unconstrained minimization problems. The new algorithm can generate an adequate trust region radius automatically at each iteration a...

In this paper, we construct a new approach of affine scaling interior algorithm using the affine scaling conjugate gradient and Lanczos methods for bound constrained nonlinear optimization. We get the iterative direction by solving quadratic model via affine scaling conjugate gradient and Lanczos methods. By using the line search backtracking technique, we will find an acceptable trial step len...

the effect of boundary conditions on the solution of the inverse problem of identifying the geometry and location of a cavity inside an elastic solid body using displacement measurements obtained from a tension test is investigated. the boundary elements method (bem) coupled with the genetic algorithm (ga) and the conjugate gradient method (cgm) are implemented in this identification problem. a...

A new approach is proposed incorporating heuristic optimization algorithm with a switch to a local search algorithm for the multidisciplinary design of solid launch vehicle at conceptual level. Heuristic optimization algorithms such as Genetic Algorithm often may locate near optimum solutions at the expense of large function evaluations. Local search algorithms including both gradient and non g...

In this paper we consider a possible improvment of conjugate gradient methods commonly used for training multilayer perceptrons. These methods rely on a line search procedure which is partially responsible for the fact that only local minima are found. We propose to perform line searches along very long lines in order to nd global minima; as usual line search procedures are not suitable in this...

A modified Polak-Ribière-Polyak conjugate gradient algorithm which satisfies both the sufficient descent condition and the conjugacy condition is presented. These properties are independent of the line search. The algorithms use the standard Wolfe line search. Under standard assumptions we show the global convergence of the algorithm. Numerical comparisons with conjugate gradient algorithms usi...

This paper presents the pre-processing of speech templates for artificial neural network (ANN). The processed features are pitch and Linear Predictive Coefficients (LPC) for input and reference templates, based on Dynamic Time Warping (DTW) algorithm. The first task is to extract pitch features using Pitch Scale Harmonic Filter algorithm. Another task is to align the input frames (test set) to ...

Unconstrained optimization problems are closely related to systems of ordinary differential equations (ODEs) with gradient structure. In this work, we prove results that apply to both areas. We analyze the convergence properties of a trust region, or Levenberg–Marquardt, algorithm for optimization. The algorithm may also be regarded as a linearized implicit Euler method with adaptive timestep f...