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Academic Articles & Research Papers
Showing 20 of 654 academic articles
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...
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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...
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DOI: https://doi.org/10.1080/01621459.1997.10474027
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...
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DOI: https://doi.org/10.2514/2.4029
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University)
(2017)
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.02515
Bing Cheng, D. Michael Titterington • Statistical Science
(1994)
This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and encourages cross-disciplinary research in the directions most likely to bear fruit. The areas...
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Open Access
DOI: https://doi.org/10.1214/ss/1177010638
Osama Moselhi, Tarek Hegazy, Paul Fazio • Journal of Construction Engineering and Management
(1991)
The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligen...
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DOI: https://doi.org/10.1061/(asce)0733-9364(1991)117:4(606)
Hecht-Nielsen • International Joint Conference on Neural Networks (IJCNN)
(1989)
The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously know...
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DOI: https://doi.org/10.1109/ijcnn.1989.118638
Laurene V. Fausett • CERN Document Server (European Organization for Nuclear Research)
(1994)
1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. Re...
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Raban Iten, Tony Metger, Henrik Wilming, Lídia del Rio et al. • Physical Review Letters
(2020)
Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture afte...
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Open Access
DOI: https://doi.org/10.1103/physrevlett.124.010508
Robert E. Uhrig • Annual Conference of the IEEE Industrial Electronics Society (IECON)
(2002)
A neural network is a data processing system consisting of a large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks ...
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DOI: https://doi.org/10.1109/iecon.1995.483329
Abhay B. Bulsari • Elsevier eBooks
(1995)
Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, ...
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• Bloomsbury Publishing Plc eBooks
(2000)
Neural Networks, Second Edition provides a complete introduction to neural networks. It describes what they are, what they can do, and how they do it. While some scientific background is assumed, the reader is not expected to have any prior...
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DOI: https://doi.org/10.5040/9781350392434
• Choice Reviews Online
(1993)
From the Publisher:
Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs pro...
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DOI: https://doi.org/10.5860/choice.31-2156
Christos Christodoulou, Michael Georgiopoulos • CERN Document Server (European Organization for Nuclear Research)
(2001)
From the Book:
Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...
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Aarush Saxena • International Journal for Research in Applied Science and Engineering Technology
(2022)
Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous for...
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Open Access
DOI: https://doi.org/10.22214/ijraset.2022.47789
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Cohen, Jonathan et al. • arXiv (Cornell University)
(2014)
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1410.0759
Santanu Pattanayak • Apress eBooks
(2017)
Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with T...
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DOI: https://doi.org/10.1007/978-1-4842-3096-1
Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong • ACM Transactions on Intelligent Systems and Technology
(2019)
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to t...
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Open Access
DOI: https://doi.org/10.1145/3298981
LIN Yan, JIN Tingyu, YANG Yuchao • Shanghai Jiaotong Daxue xuebao
(2024)
Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, ec...
DOAJ
Open Access (DOAJ)
DOI: 10.16183/j.cnki.jsjtu.2022.508
Hans van Vliet (1993)
Software Engineering: Principles and Practice challenges the reader to appreciate the issues, design trade-offs and teamwork required for successful software development. This new edition has been brought fully up to date, with complete cov...
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