A Universal Approximation Result for Difference of Log-Sum-Exp Neural Networks

On Universal Approximation Using Neural Networks

Neural Networks for Pattern Classification and Universal Approximation

LIAO, YI. Neural Networks for Pattern Classification and Universal Approximation (Under the direction of Dr. Shu-Cherng Fang and Dr. Henry L. W. Nuttle). This dissertation studies neural networks for pattern classification and universal approximation. The objective is to develop a new neural network model for pattern classification, and relax the conditions for Radial-Basis Function networks to...

Twofold exp and log

This article is about twofold arithmetic [1, 2]. Here I introduce algorithms and experimental code for twofold variant of C/C++ standard functions exp() and log(), and expm1() and log1p(). Twofold function y0 + y1 ≈ f(x0 + x1) is nearly 2x-precise so can assess accuracy of standard one. Performance allows assessing on-fly: twofold texp() over double is ~10x times faster than expq() by GNU quadm...

Global optimal solutions to nonconvex optimisation problems with a sum of double-well and log-sum-exp functions

This paper presents a canonical dual approach for solving a nonconvex global optimisation problem with a sum of double-well and log-sum-exp functions. Such a problem arises extensively in mechanics, robot designing, information theory and network communication systems. It includes fourth-order polynomial minimisation problems and minimax problems. Based on the canonical duality theory, this non...

Geoid Determination Based on Log Sigmoid Function of Artificial Neural Networks: (A case Study: Iran)

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...