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Academic Articles & Research Papers
Showing 20 of 23,220 academic articles
Gene A. Tagliarini, John Fury Christ, Edward W. Page • IEEE Transactions on Computers
(1991)
The design of feedback (or recurrent) neural networks to produce good solutions to complex optimization problems is discussed. The theoretical basis for applying neural networks to optimization problems is reviewed, and a design rule that s...
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DOI: https://doi.org/10.1109/12.106220
Ke-Lin Du, M. N. S. Swamy • Springer eBooks
(2019)
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DOI: https://doi.org/10.1007/978-1-4471-7452-3
Crina Groşan, Ajith Abraham • Intelligent systems reference library
(2011)
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DOI: https://doi.org/10.1007/978-3-642-21004-4_12
Gongbo Zhang • IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
(2000)
Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifi...
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DOI: https://doi.org/10.1109/5326.897072
Jan Ramon, Luc De Raedt • Lirias
(2000)
This paper is concerned with extending neural networks to multi-instance learning. In multi-instance learning, each example corresponds to a set of tuples in a single relation. Furthermore, examples are classied as positive if at least one ...
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M.Y. Rafiq, Guido Bugmann, Dave Easterbrook • Computers & Structures
(2001)
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DOI: https://doi.org/10.1016/s0045-7949(01)00039-6
• Choice Reviews Online
(1994)
At present, computers cannot think, make independent decisions, recognize visual and audible patterns, reach reliable conclusions without complete information or learn from their mistakes. The question this book and research into artificial...
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DOI: https://doi.org/10.5860/choice.32-2176
• Elsevier eBooks
(1998)
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DOI: https://doi.org/10.1016/b978-0-12-526420-4.x5000-9
Timothy X. Brown • IEEE Communications Magazine
(1989)
The author argues that a strong impetus for using neural networks is that they provide a framework for designing massively parallel machines. He notes that the highly interconnected architecture of switching networks suggests similarities t...
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DOI: https://doi.org/10.1109/35.41403
Hervé Abdi • Journal of Biological Systems
(1994)
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...
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DOI: https://doi.org/10.1142/s0218339094000179
Manish Kumar Mishra, Monika Srivastava • International Conference on Advances in Engineering & Technology Research
(2014)
In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications. In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because v...
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DOI: https://doi.org/10.1109/icaetr.2014.7012785
Sovan Lek, Y.-S. Park • Elsevier eBooks
(2008)
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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...
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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...
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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...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1812.08928
Bill Fulkerson, B. D. Ripley • Technometrics
(1997)
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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...
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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)
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Open Access
DOI: https://doi.org/10.1038/s43588-021-00084-1
Soteris A. Kalogirou • Applied Energy
(2000)
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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...
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DOI: https://doi.org/10.1109/37.55121