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Adaptive Resonance Theory [chapter]

1997 Principles of Artificial Neural Networks  
These results open up interesting perspectives for the application of ART networks in the study of the dynamics of learning to read.  ...  Adjustment of the learning and vigilance parameters of the network allowed us to reproduce the developmental growth of word identification performance seen in children.  ...  ART networks have several attractive features for modeling cognitive-level phenomena. First, ART is sensitive to the order in which data are presented, unlike many other neural network models.  ... 
doi:10.1142/9789812385383_0009 fatcat:45wv2htb3rczlgwlowocutcap4

Adaptive Resonance Theory [chapter]

2013 Principles of Artificial Neural Networks  
These results open up interesting perspectives for the application of ART networks in the study of the dynamics of learning to read.  ...  Adjustment of the learning and vigilance parameters of the network allowed us to reproduce the developmental growth of word identification performance seen in children.  ...  ART networks have several attractive features for modeling cognitive-level phenomena. First, ART is sensitive to the order in which data are presented, unlike many other neural network models.  ... 
doi:10.1142/9789814522748_0010 fatcat:bi5ey435pnf7hdcbwvlibceapu

Adaptive Resonance Theory [chapter]

2007 Principles of Artificial Neural Networks  
These results open up interesting perspectives for the application of ART networks in the study of the dynamics of learning to read.  ...  Adjustment of the learning and vigilance parameters of the network allowed us to reproduce the developmental growth of word identification performance seen in children.  ...  ART networks have several attractive features for modeling cognitive-level phenomena. First, ART is sensitive to the order in which data are presented, unlike many other neural network models.  ... 
doi:10.1142/9789812770578_0009 fatcat:u7xbni5rq5ag5bayyu62kqbrba

The detector principle of constructing artificial neural networks as an alternative to the connectionist paradigm [article]

Yuri Parzhin
2017 arXiv   pre-print
Artificial neural networks (ANN) are inadequate to biological neural networks.  ...  The alternative principle of ANN construction is proposed in the article. This principle was called the detector principle.  ...  -"deep neural network" (DNN)) могут иметь десятки и даже сотни слоев [9] .  ... 
arXiv:1707.03623v1 fatcat:avexlhmtynh5vlscsctpugqiqu

APPLICATION OF CLOUD-BASED SPREADSHEETS TO ARTIFICIAL NEURAL NETWORK MODELLING

Oksana Markova, Serhiy Semerikov
2019 Technology Transfer fundamental principles and innovative technical solutions  
The systematic review of their application to simulating artificial neural networks is performed.  ...  The article substantiates the necessity to develop methods of computer simulation of neural networks in the spreadsheet environment.  ...  The first description of spreadsheet application to artificial neural network simulation of visual phenomena belongs to Thomas T.  ... 
doi:10.21303/2585-6847.2019.001039 fatcat:obvc2bk32ffbxdxva6owfggj6m

Comparison of Melt Flow Index of Propylene Polymerisation in Loop Reactors using First Principles and Artificial Neural Network Models

N.F. Jumari, K.M. Yusof
2017 Chemical Engineering Transactions  
This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using first principle (FP) model and artificial neural network (ANN) model.  ...  The ANN model of the two loop reactors are developed by employing the concept of Feed- Forward Back Propagation (FFBP) network architecture using Levenberg-Marquardt training method.  ...  There are many researches that focus on developing the soft sensor using either first principle model or artificial neural network model as mention above.  ... 
doi:10.3303/cet1756028 doaj:06f151d3601149088ad0dadf33805d10 fatcat:aofpv3rubzhvtcpxtbl2opamri

Optimization and Control of a Thin Film Growth Process: A Hybrid First Principles/Artificial Neural Network Based Multiscale Modelling Approach

Donovan Chaffart, Luis A. Ricardez-Sandoval
2018 Computers and Chemical Engineering  
a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model.  ...  Highlights  A novel hybrid multiscale model is developed to simulate thin film deposition  Continuum models and stochastic PDE used to capture deposition multiscale behaviour  Artificial neural networks  ...  ACKNOWLEDGMENTS The authors of this paper would like to graciously acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC) for their financial support granted for this research  ... 
doi:10.1016/j.compchemeng.2018.08.029 fatcat:abfdtzcesfgsvaqqzcdk23elq4

Learning by Stimulation Avoidance as a Primary Principle of Spiking Neural Networks Dynamics

Lana Sinapayen, Atsushi Masumori, Nathaniel Virgo, Takashi Ikegami
2015 07/20/2015-07/24/2015  
In this paper we implement reward/punishment as the removal/application of a stimulation to a recurrent spiking neural network with spiketiming dependent plasticity.  ...  Practical implementation of the concept of reward has deep implications on what artificial-life based systems can learn and how they learn it.  ...  Acknowledgments This work was supported by Grant-in-Aid for Scientific Research (Studies on Homeo-Dynamics with Cultivated Neural Circuits and Embodied Artificial Neural Networks; 24300080).  ... 
doi:10.7551/978-0-262-33027-5-ch037 dblp:conf/ecal/SinapayenMVI15 fatcat:g32xhzitozcezdy2654v6emsae

Hybrid artificial neural network—First principle model formulation for the unsteady state simulation and analysis of a packed bed reactor for CO2 hydrogenation to methanol

G. Zahedi, A. Elkamel, A. Lohi, A. Jahanmiri, M.R. Rahimpor
2005 Chemical Engineering Journal  
The objective of this paper is to present a hybrid neural network model (NNM) for the simulation of a differential catalytic hydrogenation reactor of carbon dioxide to methanol.  ...  The bed of the reactor was assimilated to a pile of layers, each corresponding to a neural network (NN) model that can predict outlet composition of each layer as a function of time.  ...  The financial support of the Natural Sciences and Engineering Research Council (NSERC) of Canada is also gratefully acknowledged.  ... 
doi:10.1016/j.cej.2005.08.018 fatcat:43qf6urck5bvdgpvhlpurwnlvi

Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China

Yu Liu, Du-Gang Xi, Zhao-Liang Li, Kevin Scott Brown
2015 PLoS ONE  
First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle  ...  Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms.  ...  The authors greatly appreciated the careful and insightful suggestions and comments of reviewers that helped to improve the manuscript and data analysis. Author Contributions  ... 
doi:10.1371/journal.pone.0119082 pmid:25768650 pmcid:PMC4359150 fatcat:g5jwavo5lzf6vceidqtujgahzq

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se

Kohei Shimamura, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo, Masaaki Misawa, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta, Takashi Matsubara, Shigenori Tanaka
2019 Journal of Chemical Physics  
Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division, Grant # DE-SC0018195.  ...  The authors thank the Supercomputer Center, the Institute for Solid State Physics, University of Tokyo for the use of the facilities.  ...  METHOD OF CALCULATION A. Artificial Neural Network (ANN) Here, a general method of creating ANN potential is described.  ... 
doi:10.1063/1.5116420 fatcat:v2hnv2uk6ncdzmzep4uka3lz3e

Rapid and non-invasive quantification of metabolic substrates in biological cell suspensions using non-linear dielectric spectroscopy with multivariate calibration and artificial neural networks. Principles and applications

Andrew M. Woodward, Alun Jones, Xin-zhu Zhang, Jem Rowland, Douglas B. Kell
1996 Bioelectrochemistry and bioenergetics (Print)  
We exploit partial least-squares regression and artificial neural networks for the multivariate analysis of non-linear dielectric data recorded from yeast cell suspensions, and schemes for preprocessing  ...  which is indicative of the metabolic state of a variety of organisms.  ...  for support of X-z.Z., Andy A.M.  ... 
doi:10.1016/0302-4598(96)05065-9 fatcat:dcy6oqwranfj5bnmgfk4kjhh6e

The Principle of Constructing the Algorithm of the Functioning of the Neural Network - the Basis of the Exam System of Artificial Intelligence

I. Suleimenova, Alimzhan Baykenov, Tolganay Abisheva, Ilyas Kopishev
2019 Proceedings of the International Scientific Conference - Sinteza 2019   unpublished
A new approach to the development of artificial intelligence systems aimed at improving the quality of higher education in post-Soviet countries has been proposed and substantiated.  ...  of the text).  ...  Th is judgment is based on a combination of the principles of fuzzy logic with the principles of functioning of the neural network.  ... 
doi:10.15308/sinteza-2019-149-154 fatcat:vnge3iaucfg53o3rzepwadvk4y

Basics and Features of Artificial Neural Networks

Rajesh CVS, M. Padmanabham
2018 International Journal of Trend in Scientific Research and Development  
of neural network principle, artificial neural network (ANN) terminology, neuron models and topology are Biological Neural Networks, Terminology in Artificial Neural Networks, Models of Neuron and  ...  of neural network principle, artificial neural network (ANN) terminology, neuron models and topology are discussed.  ...  INTRODUCTION: In the neural network characteristics, few of the attractive features of biological neural network make superior to the most complicated in Artificial Intelligent recognition tasks.  ... 
doi:10.31142/ijtsrd9578 fatcat:qkundkxluvambeeka3q645vkbm

Artificial Neural Network and Its Application Research Progress in Chemical Process [article]

Li Sun, Fei Liang, Wutai Cui
2021 arXiv   pre-print
This article will introduce the basic principles and development history of artificial neural networks, and review its application research progress in chemical process control, fault diagnosis, and process  ...  Artificial neural network (ANN) is a systematic structure composed of multiple neuron models. Its main function is to simulate multiple basic functions of the nervous system of living organisms.  ...  Principles and development history of artificial neural networks The artificial neural network imitates the human brain neuron network and abstracts it, and then establishes a certain mathematical model  ... 
arXiv:2110.09021v1 fatcat:zbkgxxpjsbcgxbp6vt4mwqr2za
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