A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks

Dazuo Yang; Hao Li; Fudi Chen; Yibing Zhou; Zhilong Xiu

doi:10.1109/IWECA.2014.6845686

A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks

Dazuo Yang, Hao Li, Fudi Chen, Yibing Zhou, Zhilong Xiu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Dulong-Petit law is an ordinary description of specific heat capacity, which states that the heat capacity per weight (i.e., mass-specific heat capacity) for a number of substances becomes close to a constant value. In our study, we trained 30 groups' data of metal elementary substances to establish a general regression neural network (GRNN) model within NeuralTools Software to predict the constant of the Dulong-Petit law. We used 31 samples to test the robustness of the computer model. In our results, 100% of the tested samples showed accurate results within the permissible error range (30% tolerance).Based on the characteristic of the artificial neural network (ANN) model established by NeuralTools, we applied our model to analyze the weight of different independent variables and test the accuracy of the Dulong-Petit law qualitatively. Finally, we put forward a novel proof method to support the theories and laws of natural science using the ANN model.

Original language	English
Title of host publication	Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014
Publisher	IEEE Computer Society
Pages	577-580
Number of pages	4
ISBN (Print)	9781479945658
DOIs	https://doi.org/10.1109/IWECA.2014.6845686
Publication status	Published - 2014
Externally published	Yes
Event	2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014 - Ottawa, ON, Canada Duration: 2014 May 8 → 2014 May 9

Publication series

Name	Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014

Conference

Conference	2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014
Country/Territory	Canada
City	Ottawa, ON
Period	14/5/8 → 14/5/9

Keywords

Dulong-Petit law
Specific heat capacity
artificial neural networks
general regression neural networks
proof method

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/IWECA.2014.6845686

Cite this

Yang, D., Li, H., Chen, F., Zhou, Y., & Xiu, Z. (2014). A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks. In Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014 (pp. 577-580). [6845686] (Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014). IEEE Computer Society. https://doi.org/10.1109/IWECA.2014.6845686

A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks. / Yang, Dazuo; Li, Hao; Chen, Fudi et al.
Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014. IEEE Computer Society, 2014. p. 577-580 6845686 (Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, D, Li, H, Chen, F, Zhou, Y & Xiu, Z 2014, A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks. in Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014., 6845686, Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014, IEEE Computer Society, pp. 577-580, 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014, Ottawa, ON, Canada, 14/5/8. https://doi.org/10.1109/IWECA.2014.6845686

Yang D, Li H, Chen F, Zhou Y, Xiu Z. A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks. In Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014. IEEE Computer Society. 2014. p. 577-580. 6845686. (Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014). doi: 10.1109/IWECA.2014.6845686

@inproceedings{2c4c679a4228421282a1c8adde3529d4,

title = "A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks",

abstract = "Dulong-Petit law is an ordinary description of specific heat capacity, which states that the heat capacity per weight (i.e., mass-specific heat capacity) for a number of substances becomes close to a constant value. In our study, we trained 30 groups' data of metal elementary substances to establish a general regression neural network (GRNN) model within NeuralTools Software to predict the constant of the Dulong-Petit law. We used 31 samples to test the robustness of the computer model. In our results, 100% of the tested samples showed accurate results within the permissible error range (30% tolerance).Based on the characteristic of the artificial neural network (ANN) model established by NeuralTools, we applied our model to analyze the weight of different independent variables and test the accuracy of the Dulong-Petit law qualitatively. Finally, we put forward a novel proof method to support the theories and laws of natural science using the ANN model.",

keywords = "Dulong-Petit law, Specific heat capacity, artificial neural networks, general regression neural networks, proof method",

author = "Dazuo Yang and Hao Li and Fudi Chen and Yibing Zhou and Zhilong Xiu",

year = "2014",

doi = "10.1109/IWECA.2014.6845686",

language = "English",

isbn = "9781479945658",

series = "Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014",

publisher = "IEEE Computer Society",

pages = "577--580",

booktitle = "Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014",

note = "2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014 ; Conference date: 08-05-2014 Through 09-05-2014",

}

TY - GEN

T1 - A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks

AU - Yang, Dazuo

AU - Li, Hao

AU - Chen, Fudi

AU - Zhou, Yibing

AU - Xiu, Zhilong

PY - 2014

Y1 - 2014

N2 - Dulong-Petit law is an ordinary description of specific heat capacity, which states that the heat capacity per weight (i.e., mass-specific heat capacity) for a number of substances becomes close to a constant value. In our study, we trained 30 groups' data of metal elementary substances to establish a general regression neural network (GRNN) model within NeuralTools Software to predict the constant of the Dulong-Petit law. We used 31 samples to test the robustness of the computer model. In our results, 100% of the tested samples showed accurate results within the permissible error range (30% tolerance).Based on the characteristic of the artificial neural network (ANN) model established by NeuralTools, we applied our model to analyze the weight of different independent variables and test the accuracy of the Dulong-Petit law qualitatively. Finally, we put forward a novel proof method to support the theories and laws of natural science using the ANN model.

AB - Dulong-Petit law is an ordinary description of specific heat capacity, which states that the heat capacity per weight (i.e., mass-specific heat capacity) for a number of substances becomes close to a constant value. In our study, we trained 30 groups' data of metal elementary substances to establish a general regression neural network (GRNN) model within NeuralTools Software to predict the constant of the Dulong-Petit law. We used 31 samples to test the robustness of the computer model. In our results, 100% of the tested samples showed accurate results within the permissible error range (30% tolerance).Based on the characteristic of the artificial neural network (ANN) model established by NeuralTools, we applied our model to analyze the weight of different independent variables and test the accuracy of the Dulong-Petit law qualitatively. Finally, we put forward a novel proof method to support the theories and laws of natural science using the ANN model.

KW - Dulong-Petit law

KW - Specific heat capacity

KW - artificial neural networks

KW - general regression neural networks

KW - proof method

UR - http://www.scopus.com/inward/record.url?scp=84904547575&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904547575&partnerID=8YFLogxK

U2 - 10.1109/IWECA.2014.6845686

DO - 10.1109/IWECA.2014.6845686

M3 - Conference contribution

AN - SCOPUS:84904547575

SN - 9781479945658

T3 - Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014

SP - 577

EP - 580

BT - Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014

PB - IEEE Computer Society

T2 - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014

Y2 - 8 May 2014 through 9 May 2014

ER -

A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this