An approach to measure the densities of solids using an artificial neural network

被引：1
|
作者：
Neelamegam, P.
Rajendran, A. [1 ]
机构：
[1] Nehru Mem Coll Autonomous, PG & Res Dept Appl Phys, Tiruchirappalli, Tamil Nadu, India
[2] Deemed Univ, SASTRA, Dept Elect & Instrumentat Engn, Thanjavur, Tamil Nadu, India
来源：
INSTRUMENTATION SCIENCE & TECHNOLOGY | 2007年 / 35卷 / 02期
关键词：
density measurement; temperature measurement; microcontroller; neural network; back propagation algorithm;
D O I：
10.1080/10739140601126452
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
A dedicated microcontroller based density measurement system is developed to measure densities of solids. A data acquisition system is designed and fabricated using a PIC16F877 microcontroller. To measure the weight and temperature of the sample, strain gauge and thermocouple sensors are used. A three layer neural network is used to train the data for atomic number, temperature, and density of sample using a back propagation algorithm. After training the neural network, it is used to compute the density at various temperatures.
引用
收藏
页码：189 / 199
页数：11
相关论文
共 50 条
  • [21] NEWS ARTICLE CLASSIFICATION USING KOLMOGOROV COMPLEXITY DISTANCE MEASURE AND ARTIFICIAL NEURAL NETWORK
    Fagbola, Temitayo Matthew
    Thakur, Colin Surendra
    Olugbara, Oludayo
    INTERNATIONAL JOURNAL OF TECHNOLOGY, 2019, 10 (04) : 710 - 720
  • [22] An Event Classifier using EEG Signals: an Artificial Neural Network Approach
    Nawroj, Ahsan
    Wang, Siyuan
    Jouny, Ismail
    Yu, Yih-Choung
    Gabel, Lisa
    2012 38TH ANNUAL NORTHEAST BIOENGINEERING CONFERENCE (NEBEC), 2012, : 386 - +
  • [23] ESTIMATION OF LIQUIDUS TEMPERATURES OF STEEL USING ARTIFICIAL NEURAL NETWORK APPROACH
    Machu, Mario
    Drozdova, Lubomira
    Smetana, Bedrich
    Zimny, Ondrej
    Vlcek, Jozef
    27TH INTERNATIONAL CONFERENCE ON METALLURGY AND MATERIALS (METAL 2018), 2018, : 56 - 62
  • [24] Locus minimization in breed prediction using artificial neural network approach
    Iquebal, M. A.
    Ansari, M. S.
    Sarika
    Dixit, S. P.
    Verma, N. K.
    Aggarwal, R. A. K.
    Jayakumar, S.
    Rai, A.
    Kumar, D.
    ANIMAL GENETICS, 2014, 45 (06) : 898 - 902
  • [25] Classification and analysis of simple pendulum using artificial neural network approach
    Wadhwa, Adya
    Wadhwa, Ajay
    EUROPEAN JOURNAL OF PHYSICS, 2024, 45 (06)
  • [26] Predicting bacterial community assemblages using an artificial neural network approach
    Larsen, Peter E.
    Field, Dawn
    Gilbert, Jack A.
    NATURE METHODS, 2012, 9 (06) : 621 - +
  • [27] Rainfall-runoff model using an artificial neural network approach
    Riad, S
    Mania, J
    Bouchaou, L
    Najjar, Y
    MATHEMATICAL AND COMPUTER MODELLING, 2004, 40 (7-8) : 839 - 846
  • [28] Performance evaluation of air ejectors using artificial neural network approach
    Gupta, Pradeep
    Rao, Srisha M., V
    Kumar, Pramod
    SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2023, 48 (02):
  • [29] New Approach to Optimize the Rate of Penetration Using Artificial Neural Network
    Elkatatny, Salaheldin
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (11) : 6297 - 6304
  • [30] Predicting bacterial community assemblages using an artificial neural network approach
    Larsen P.E.
    Field D.
    Gilbert J.A.
    Nature Methods, 2012, 9 (6) : 621 - 625
12345