By Means of Artificial Neural NetworksNeuner, H
The difference between experimental and predicted outputs is usually quantified by means of an error function similar to those used in statistics. In different research fields, it has been proposed that the performance of a well-designed MLP network is comparable with that achieved using classical ...
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption...
Solution to Inverse Heat Transfer Problems by Means of Soft Computing Approach and Its Comparison to the Well-Established Beck's Method ARTIFICIAL neural networksMany engineering problems involve heat transfer with phase change and their solution often lead to challenging heat transfer problems ... L ...
The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, whi
(Atkinson et al.,2005; Atkinson & Bench-Capon,2007,2016,2021). These could help us to find new, different solutions, that come at a problem from a novel angle. And when evaluating these alternatives, we may choose to formulate the very notion of what ‘successful’ means according to ...
There has been an increasing interest in using neural networks to model and forecast time series over the last decade. They have been found to be a viable contender to various traditional time series models [12], [18], [26]. Several distinguishing features of ANNs make them valuable and ...
Chapter 1. Introduction to Artificial Neural Networks Birds inspired us to fly, burdock plants inspired velcro, and nature has inspired many other inventions. It seems only logical, then, to look … - Selection from Neural networks and deep learning [Bo
Evolving medical adaptation of artificial intelligence (AI) proffers the means to investigate nonlinear data relationships, enhance data interpretation, and design more efficient diagnostic and predictive tool10. ANN, an AI method simulating the structure and functionalities of the human neural architecture...
Adversarial training is not the only means of making models adversarially robust. But when examining other sources of robustness, we again found examples where a model’s robustness was not predictive of the recognizability of its metamers. Here, we present results for two models with similar robu...