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Building neural networks

David M. Skapura (1995)

Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagatio...

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Determining the Optimum Layer Combination for Cross-Laminated Timber Panels According to Timber Strength Classes Using Artificial Neural Networks

Engin Derya Gezer, Abdullah Uğur Birinci, Aydın Demir, Hasan Öztürk et al. • BioResources (2024)

The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN)...

DOAJ Open Access (DOAJ)

Surrogate Modeling of the Electric Field in the End-Winding Region of Pumped-Storage Generator Stators Based on Deep Neural Networks

Chunxu Qin, Yiran Ma, Huijuan Liang, Zhifan Wang et al. • Modelling (2026)

The end-winding insulation structure of stator windings in pumped-storage generator units is complex, with pronounced electric field concentration under out-of-phase conditions, making them critical concerns in insulation design and conditi...

DOAJ Open Access (DOAJ) DOI: 10.3390/modelling7050190

A neural network model for bankruptcy prediction

Marcus D. Odom, Ramesh Sharda • International Joint Conference on Neural Networks (IJCNN) (1990)

A neural network model is developed for prediction of bankruptcy, and it is tested using financial data from various companies. The same set of data is analyzed using a more traditional method of bankruptcy prediction, multivariate discrimi...

OPENALEX DOI: https://doi.org/10.1109/ijcnn.1990.137710

Incorporating structural plasticity into self-organization recurrent networks for sequence learning

Ye Yuan, Yongtong Zhu, Jiaqi Wang, Ruoshi Li et al. • Frontiers in Neuroscience (2023)

IntroductionSpiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-depen...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2023.1224752

Neural Networks for Chemists

Qingyang Xiao, Kaiyuan Liu, Yuhui Hong, Haixu Tang • ACS in focus (2024)

In recent years, breakthroughs in artificial intelligence (AI) have dramatically transformed many fields. Developments in neural networks primarily drive the advances. These powerful models have demonstrated unprecedented capabilities in le...

OPENALEX DOI: https://doi.org/10.1021/acsinfocus.7e8012

Shear banding and flow instabilities in wormlike micelles: Modelling and mechanisms – A review

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

Neural Networks in Chemistry

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

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

Su-Hsin Huang, Joon-Khim Loh, Jinn-Tsong Tsai, Ming-Feng Houg et al. • Chinese Journal of Cancer (2017)

Abstract Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40880-017-0192-9

Artificial Neural Networks

Hojjat Adeli, Xiaomo Jiang • CRC Press eBooks (2008)

An artificial neural network (ANN) is a functional abstraction of the biologic neural structures of the central nervous system (Aleksander and Morton 1993; Rudomin et al. 1993; Arbib 1995; Anderson 1995). It is essentially a simple mathemat...

OPENALEX DOI: https://doi.org/10.1201/9781482281767-9

On Calibration of Modern Neural Networks

Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger • arXiv (Cornell University) (2017)

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1706.04599

Monthly Evapotranspiration Modeling in the Absence of Climatic Data Using the Soft Computing Methods in West and Northwest of Iran

Saman Maroufpour, Eisa Maroufpoor, Hadi Sanikhani • مهندسی منابع آب (2017)

Evapotranspiration (ET0), a major component of the hydrologic cycle, is important in water resources development and irrigation planning. The ET0 for west and northwest of Iran was estimated using the FAO Penman-Montieth method (FAO-56) as ...

DOAJ Open Access (DOAJ)

Artificial neural networks

Judith E. Dayhoff, James M. DeLeo • Cancer (2001)

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...

OPENALEX Open Access DOI: https://doi.org/10.1002/1097-0142(20010415)91:8+<1615::aid-cncr1175>3.0.co;2-l

Neural Network Learning

Martin Anthony, Peter L. Bartlett • Cambridge University Press eBooks (1999)

First published in 1999, this book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. R...

OPENALEX DOI: https://doi.org/10.1017/cbo9780511624216

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Enrique González-Plaza, David García, Jesús-Ignacio Prieto • Inventions (2023)

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

DOAJ Open Access (DOAJ) DOI: 10.3390/inventions8040088

CL-BPUWM: continuous learning with Bayesian parameter updating and weight memory

Yao He, Jing Yang, Shaobo Li, Jianjun Hu et al. • Complex & Intelligent Systems (2024)

Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the pa...

DOAJ Open Access (DOAJ) DOI: 10.1007/s40747-024-01350-1