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Optimization using neural networks

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...

OPENALEX DOI: https://doi.org/10.1109/12.106220

Neural networks for classification: a survey

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...

OPENALEX DOI: https://doi.org/10.1109/5326.897072

Multi instance neural networks

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 ...

OPENALEX other-oa

Neural network computing

• 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...

OPENALEX DOI: https://doi.org/10.5860/choice.32-2176

Neural networks for switching

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...

OPENALEX DOI: https://doi.org/10.1109/35.41403

A NEURAL NETWORK PRIMER

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...

OPENALEX DOI: https://doi.org/10.1142/s0218339094000179

A view of Artificial Neural Network

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...

OPENALEX DOI: https://doi.org/10.1109/icaetr.2014.7012785

Photonic Neural Networks: A Survey

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 cc-by DOI: https://doi.org/10.1109/access.2019.2957245

Artificial Neural Networks in Agriculture

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 cc-by DOI: https://doi.org/10.3390/agriculture11060497

Slimmable Neural Networks

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

Artificial Neural Networks

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

Neural networks for system identification

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