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Showing 20 of 1,964 academic articles
Dan W. Hammerstrom • IEEE Spectrum
(1993)
The benefits of neural networks and the types of application for which they are suited are outlined. Four representative applications are described in enough detail to show how they work. They are character recognition, function estimation,...
OPENALEX
DOI: https://doi.org/10.1109/6.214579
Timothy D. Masters (1993)
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to prod...
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Seyedali Mirjalili • Studies in computational intelligence
(2018)
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DOI: https://doi.org/10.1007/978-3-319-93025-1
Hava T. Siegelmann • Birkhäuser Boston eBooks
(1999)
The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnecte...
OPENALEX
DOI: https://doi.org/10.1007/978-1-4612-0707-8
Eytan Domany, J. Leo Van Hammen, Klaus Schulten • CERN Document Server (European Organization for Nuclear Research)
(1991)
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DOI: https://doi.org/10.1007/978-1-4612-0723-8
Kevin Robert Gurney • CRC Press eBooks
(2018)
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counte...
OPENALEX
DOI: https://doi.org/10.1201/9781315273570
Dan W. Hammerstrom • IEEE Spectrum
(1993)
As explanation of how neural networks operate is given. Programming of neural networks and regular computers is compared. Matching a neural network to an application is discussed. Designing and training a neural network is examined.>...
OPENALEX
DOI: https://doi.org/10.1109/6.222230
K.J. Hunt, Daniel G. Sbarbaro, R. Żbikowski, P.J. Gawthrop • Automatica
(1992)
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DOI: https://doi.org/10.1016/0005-1098(92)90053-i
Andrej Krenker, Janez Bešter, Andrej Kos • InTech eBooks
(2011)
An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. Basic building block of every artificial neural network is artificial neuron, that is, a simp...
OPENALEX
cc-by-nc-sa
DOI: https://doi.org/10.5772/15751
LiMin Fu (1994)
From the Publisher:
Neural Networks in Computer Intelligence provides basic concepts,algorithms,and analysis of important neural network models developed to date,with emphasis on the importance of knowledge in intelligent system design. Th...
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Berndt Müller, J. Reinhardt • Physics of neural networks
(1990)
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DOI: https://doi.org/10.1007/978-3-642-97239-3
Michael Peter Kennedy, Leon Ong Chua • IEEE Transactions on Circuits and Systems
(1988)
The dynamics of the modified canonical nonlinear programming circuit are studied and how to guarantee the stability of the network's solution. By considering the total cocontent function, the solution of the canonical nonlinear programming ...
OPENALEX
DOI: https://doi.org/10.1109/31.1783
Jinming Zou, Yi Han, Sung‐Sau So • Methods in molecular biology
(2008)
The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to ...
OPENALEX
DOI: https://doi.org/10.1007/978-1-60327-101-1_2
Kai Chen, Pengtao Zhang, Liang You, Jian Sun • Applied Sciences
(2024)
In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite Sy...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app14135493
RT Pramod, Michael A Cohen, Joshua B Tenenbaum, Nancy Kanwisher • eLife
(2022)
Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and henc...
DOAJ
Open Access (DOAJ)
DOI: 10.7554/eLife.71736
Sudheesh Parathakkatt, Vaisakh Kizhuveetil, Gokul G. K., Rajashree Ajay Markandewar • eXPRESS Polymer Letters
(2026)
Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review pro...
DOAJ
Open Access (DOAJ)
DOI: 10.3144/expresspolymlett.2026.36
Johann Gasteiger, Jure Zupan • Angewandte Chemie International Edition in English
(1993)
Abstract The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's vario...
OPENALEX
DOI: https://doi.org/10.1002/anie.199305031
Genevieve Orr, Müller, Klaus-Robert • Lecture notes in computer science
(1998)
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DOI: https://doi.org/10.1007/3-540-49430-8
Yuehao Pan, Weimin Huang, Zhiping Lin, Wanzheng Zhu et al. • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
(2015)
This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, wit...
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DOI: https://doi.org/10.1109/embc.2015.7318458
Miroslav Kubát • Springer eBooks
(2017)
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DOI: https://doi.org/10.1007/978-3-319-63913-0_5