Prediction of dynamic features is an important task for determining the manipulation strategies of an object. This paper presents a technique for predicting dynamics of objects re...
Shun Nishide, Tetsuya Ogata, Jun Tani, Kazunori Ko...
—This paper studies the problem of understanding noisy and structurally deformed two-dimensional images by means of abstractly defined neural works. First, in the framework of sy...
— In this paper, a neural network approach is presented to expand the Pareto-optimal front for multiobjective optimization problems. The network is trained using results obtained...
Face localization using neural network is presented in this communication. Neural network was trained with two different kinds of feature parameters vectors; Zernike moments and E...
Mohammed Saaidia, Anis Chaari, Sylvie Lelandais, V...
This work presents an implementation of Neocognitron Neural Network, using a high performance computing architecture based on GPU (Graphics Processing Unit). Neocognitron is an ar...
— Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional ...
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
— Aircraft noise is influenced by many complex factors and it is difficult to devise an accurate mathematical model to simulate it with respect to operations at an airport. Thi...
Abstract— This paper introduces a novel design of an artificial neural network tailored for wafer-scale integration. The presented VLSI implementation includes continuous-time a...
Johannes Schemmel, Johannes Fieres, Karlheinz Meie...
—The dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of compu...