Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.Comment: 14 Pages, 11 figure

An Improved Gauss-Newtons Method based Back-propagation Algorithm for Fast Convergence

The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The efficacy of the proposed method is observed during the training period as it converges quickly for the dataset used in test. The requirement of memory for computing the steps of algorithm is also analyzed.Comment: 7 pages, 6 figures,2 tables, Published with International Journal of Computer Applications (IJCA

Improved infrared photoluminescence characteristics from circularly ordered self-assembled Ge islands

The formation of circularly ordered Ge-islands on Si(001) has been achieved because of nonuniform strain field around the periphery of the holes patterned by focused ion beam in combination with a self-assembled growth using molecular beam epitaxy. The photoluminescence (PL) spectra obtained from patterned areas (i.e., ordered islands) show a significant signal enhancement, which sustained till 200 K, without any vertical stacking of islands. The origin of two activation energies in temperature-dependent PL spectra of the ordered islands has been explained in detail

Health Assessment of Large Two Dimensional Structures Using Limited Information: Recent Advances

Some recent advances of a recently developed structural health assessment procedure proposed by the research team at the University of Arizona, commonly known as generalized iterative least-squares extended Kalman filter with unknown input (GILS-EKF-UI) are presented. The procedure is a finite elements-based time-domain system-identification technique. It can assess structural health at the element level using only limited number of noise-contaminated responses. With the help of examples, it is demonstrated that the structure can be excited by multiple loadings simultaneously. The method can identify defects in various stages of degradation in single or multiple members and also relatively less severe defect. The defective element(s) need not be in the substructure, but the defect detection capability increases if the defect spot is close to the substructure. Two alternatives are suggested to locate defect spot more accurately within a defective element. The paper advances several areas of GILS-EKF-UI to assess health of large structural systems