This note questions the behavioral content of second-order acts and their use in decision theoretic models. We show that there can be no verification mechanism to determine what the decision maker receives under a second-order act. This impossibility applies even in idealized repeated experiments where infinite data can be observed. ∗We thank Paolo Ghirardato, Ben Polak, Kyoungwon Seo, and Marc...

In recent years, Computer Aided Design (CAD)based on Artificial Neural Networks (ANNs) have been introduced for microwave modeling simulation and optimization. In this paper, the characteristic parameters of Broadside Coupled Coplanar Waveguides (BSCCPWs) have been determined with the use of ANN model. Eight learning algorithms, Levenberg Marquart(LM), Bayesian Regularization (BR),Quasi–Newton ...

The method of shortest residuals (SR) was presented by Hestenes and studied by Pitlak. If the function is quadratic, and if the line search is exact, then the SR method reduces to the linear conjugate gradient method. In this paper, we put forward the formulation of the SR method when the line search is inexact. We prove that, if stepsizes satisfy the strong Wolfe conditions, both the Fletcher-...

Most evolutionary algorithms ultimately focus on optimizing solutions to a single target function, coevolution and related methods notwithstanding. Cooptive phenomena between organisms adapted to distinct environmental niches, however, lie at the heart of the evolution of complex functions in nature and technology, where solutions adapted for one problem are repurposed to solve another, related...

Copyright © 2007 American Heart Association. All rights reserved. Print ISSN: 0009-7322. Online 72514 Circulation is published by the American Heart Association. 7272 Greenville Avenue, Dallas, TX DOI: 10.1161/CIRCULATIONAHA.106.645606 2007;116;32-38; originally published online Jun 18, 2007; Circulation H. Hirsch, Lewis H. Kuller and Mary Cushman Jie J. Cao, Alice M. Arnold, Teri A. Manolio, J...

In this article, we explore the effectiveness of different numerical techniques in the training of backpropaqgation neural networks (BPNN) which are fed with wavelet-transformed data to capture useful information on various time scales. The purpose is to predict S&P500 future prices using BPNN trained with conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), ...