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Showing 20 of 4,934 academic articles
Sovan Lek, Y.-S. Park • Elsevier eBooks
(2008)
OPENALEX
DOI: https://doi.org/10.1016/b978-008045405-4.00173-7
Lorenzo De Marinis, Marco Cococcioni, P. Castoldi, Nicola Andriolli • IEEE Access
(2019)
Photonic solutions are today a mature industrial reality concerning high speed, high throughput data communication and switching infrastructures. It is still a matter of investigation to what extent photonics will play a role in next-genera...
OPENALEX
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DOI: https://doi.org/10.1109/access.2019.2957245
Sebastian Kujawa, Gniewko Niedbała • Agriculture
(2021)
Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple proc...
OPENALEX
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DOI: https://doi.org/10.3390/agriculture11060497
Jiahui Yu, Linjie Yang, Ning Xu, Yang, Jianchao et al. • arXiv (Cornell University)
(2018)
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1812.08928
Bill Fulkerson, B. D. Ripley • Technometrics
(1997)
OPENALEX
DOI: https://doi.org/10.1080/00401706.1997.10485099
Steven Walczak • Advances in computer and electrical engineering book series
(2018)
This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step...
OPENALEX
DOI: https://doi.org/10.4018/978-1-5225-7368-5.ch004
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi et al. • Nature Computational Science
(2021)
OPENALEX
Open Access
DOI: https://doi.org/10.1038/s43588-021-00084-1
Soteris A. Kalogirou • Applied Energy
(2000)
OPENALEX
Open Access
DOI: https://doi.org/10.1016/s0306-2619(00)00005-2
S. Reynold Chu, Rahmat A. Shoureshi, Manoel Fernando Tenorio • IEEE Control Systems Magazine
(1990)
Two approaches are presented for utilization of neural networks in identification of dynamical systems. In the first approach, a Hopfield network is used to implement a least-squares estimation for time-varying and time-invariant systems. T...
OPENALEX
DOI: https://doi.org/10.1109/37.55121
Marius-Constantin Popescu, Valentina Emilia Bălaş, Liliana Perescu-Popescu, Nikos E. Mastorakis (2009)
Abstract:- The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning ...
OPENALEX
DOI: https://doi.org/10.5555/1639537.1639542
Qiming Zhang, Haoyi Yu, Martina Barbiero, Baokai Wang et al. • Light Science & Applications
(2019)
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. Nano...
OPENALEX
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DOI: https://doi.org/10.1038/s41377-019-0151-0
PMC: 6504946
Mike Schuster, Kuldip K. Paliwal • IEEE Transactions on Signal Processing
(1997)
In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future f...
OPENALEX
DOI: https://doi.org/10.1109/78.650093
C. H. Chen • McGraw-Hill, Inc. eBooks
(1996)
Fuzzy logic principles and algorithms design guidelines for fuzzy logic systems enhanced training algorithms maximum likelihood training temporal difference learning NADINE hybrid learning systems explanation and reasoning within connectivi...
OPENALEX
R. Frank, N. Davey, S. P. Hunt • Journal of Intelligent & Robotic Systems
(2001)
OPENALEX
Open Access
DOI: https://doi.org/10.1023/a:1012074215150
Gavin Taylor • John Wiley & Sons, Inc. eBooks
(1996)
From the Publisher:
The chapters in this volume are an indication of the vigour and strength of the subject of neural networks in solving hard problems. They also indicate how the subject is giving new insights and developing new relations...
OPENALEX
J. T. Gene Hwang, Aidong Adam Ding • Journal of the American Statistical Association
(1997)
The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data...
OPENALEX
DOI: https://doi.org/10.1080/01621459.1997.10474027
Iris Cong, Soonwon Choi, Mikhail D. Lukin • Nature Physics
(2019)
OPENALEX
Open Access
DOI: https://doi.org/10.1038/s41567-019-0648-8
Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics
(1997)
The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a ight control architecture based on feedback linearization of the aircraft...
OPENALEX
DOI: https://doi.org/10.2514/2.4029
Subana Shanmuganathan, Sandhya Samarasinghe • Studies in computational intelligence
(2016)
OPENALEX
DOI: https://doi.org/10.1007/978-3-319-28495-8
Diego Andina, Antonio Vega-Corona, Juan Seijas, Martín Javier Alarcón Mondéjar • Springer eBooks
(2007)
OPENALEX
DOI: https://doi.org/10.1007/0-387-37452-3_4