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Showing 20 of 227 academic articles

Evaluation of Indoor Thermal Environments Using a Novel Predicted Mean Vote Model Based on Artificial Neural Networks

Xiaosai Duan, Suihuai Yu, Jianjie Chu, Dengkai Chen et al. • Buildings (2022)

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefo...

DOAJ Open Access (DOAJ) DOI: 10.3390/buildings12111880

Neural Networks for River Flow Prediction

Nachimuthu Karunanithi, William J. Grenney, Darrell Whitley, Ken D. Bovee • Journal of Computing in Civil Engineering (1994)

The surface‐water hydrographs of rivers exhibit large variations due to many natural phenomena. One of the most commonly used approaches for interpolating and extending streamflow records is to fit observed data with an analytic power model...

OPENALEX DOI: https://doi.org/10.1061/(asce)0887-3801(1994)8:2(201)

Bayesian Regularization of Neural Networks

Frank R. Burden, David Alan Winkler • Methods in molecular biology (2008)

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...

OPENALEX DOI: https://doi.org/10.1007/978-1-60327-101-1_3

Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach

Sundaram Senthilraj, Ramachandran Raja, Jinde Cao, Habib M. Fardoun • Nonlinear Analysis (2019)

This paper focuses on the problem of delay-dependent robust dissipativity analysis for a class of stochastic fuzzy neural networks with time-varying delay. The randomly occurring uncertainties under consideration are assumed to follow certa...

DOAJ Open Access (DOAJ) DOI: 10.15388/NA.2019.4.5

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

Digital Local Return Services and Purchase Intention in Cross-Border E-Commerce: A Risk–Trust Perspective

Xianfa Shi, Miao Su, Keun-sik Park • Journal of Theoretical and Applied Electronic Commerce Research (2026)

Cross-border e-commerce offers consumers broader product access, yet uncertainty surrounding returns continues to suppress online purchase decisions. This study conceptualizes digital local return services as a digital assurance mechanism i...

DOAJ Open Access (DOAJ) DOI: 10.3390/jtaer21060165

Neural networks for nonlinear programming

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

Overview of Artificial Neural Networks

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

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

A Complex Network Epidemiological Approach for Infectious Disease Spread Control with Time-Varying Connections

Alma Y. Alanis, Gustavo Munoz-Gomez, Nancy F. Ramirez, Oscar D. Sanchez et al. • Algorithms (2024)

This work introduces an impulsive neural control algorithm designed to mitigate the spread of epidemic diseases. The objective of this paper is the development of a vaccination strategy based on a PIN-type impulsive controller based on an o...

DOAJ Open Access (DOAJ) DOI: 10.3390/a17120577

Kalman Filtering and Neural Networks

• Wiley eBooks (2001)

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear appr...

OPENALEX DOI: https://doi.org/10.1002/0471221546

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

ACTIVATION FUNCTIONS IN NEURAL NETWORKS

Siddharth Sharma, Simone Sharma, Anidhya Athaiya • International Journal of Engineering Applied Sciences and Technology (2020)

Artificial Neural Networks are inspired from the human brain and the network of neurons present in the brain.The information is processed and passed on from one neuron to another through neuro synaptic junctions.Similarly, in artificial neu...

OPENALEX Open Access DOI: https://doi.org/10.33564/ijeast.2020.v04i12.054

Understanding the role of autoencoders for stiff dynamical systems using information theory

Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez, Hong G. Im • Energy and AI (2025)

Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....

DOAJ Open Access (DOAJ) DOI: 10.1016/j.egyai.2025.100567

Deep learning for universal linear embeddings of nonlinear dynamics

Bethany Lusch, J. Nathan Kutz, Steven L. Brunton • Nature Communications (2018)

Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator is a leading data-driven e...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-018-07210-0 PMC: 6251871

Deep Learning

• De Gruyter eBooks (2020)

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep...

OPENALEX DOI: https://doi.org/10.1515/9783110670905

Greedy Broad Learning System

Weitong Ding, Yubo Tian, Shudan Han, Huining Yuan • IEEE Access (2021)

In order to overcome the extremely time-consuming drawback of deep learning (DL), broad learning system (BLS) was proposed as an alternative method. This model is simple, fast, and easy to update. To ensure the fitting and generalization ab...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2021.3084610