Information Technology of Estimation and Forecasting Innovative Activity Based on Distributed Data Input

Dmitriy Nesteruk; Mikhail Momot

doi:10.4028/www.scientific.net/AMM.682.579

Paper Titles

The Organization of Engineering Shop “Lean” Management System
p.555

Structurization of Data and Knowledge for the Information Technology of Road-Climatic Zoning
p.561

State Regulation of Investment Activities in Russian Mechanical Engineering
p.567

The Model of Strategic Planning at the Enterprise with Process Management Approach
p.572

Information Technology of Estimation and Forecasting Innovative Activity Based on Distributed Data Input
p.579

Russian Coal Industry Amid Global Financial Crisis in 1998 and 2008
p.586

The Problems of Developing the Ecological Market of the Region
p.591

Algorithm of Controlling Servo Drives by Means of Atmega 8 Microcontroller
p.596

A Model of Decision Support on Migration of Enterprise IT-Applications in the Cloud Environment
p.600

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 682Information Technology of Estimation and...

Information Technology of Estimation and Forecasting Innovative Activity Based on Distributed Data Input

Abstract:

The usage of the neuronetwork model of experts’ interaction in forecasting the influence of results of innovation’s industrial implantation is offered. The concept of the web oriented expert system construction is proved.

You might also be interested in these eBooks

Innovative Technologies and Economics in Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 682)

Pages:

579-585

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.682.579

Citation:

Cite this paper

Online since:

October 2014

Authors:

Dmitriy Nesteruk*, Mikhail Momot

Keywords:

Algorithm (GA), Expert, Expert System, Innovation, Neuronetwork Mode

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Godet М. Reducing the Blunders in Forecasting / Futures, 1983. — 15, № 3. — P. 181–192.

DOI: 10.1016/0016-3287(83)90164-7

Google Scholar

[2] Cohen, W. (1996). Empirical studies of innovative activity,. in P. Stoneman (ed. ), The Handbook of the Economics of Technological Change, Oxford: Basil Blackwell, 182-264.

Google Scholar

[3] Rybina G.V. Bases of intellectual systems construction. – M.: Finansy i statistika; INFRA-M, 2010. 432 p.

Google Scholar

[4] Rowe, Gene & Wright, George (2001). Expert opinions in forecasting: The role of the Delphi technique, In: J. S. Armstrong (Ed. ), Principles of Forecasting. Boston: Kluwer Academic Publishers.

Google Scholar

[5] Nesteruk D.N. Monitoring of innovative activity efficiency / Kreativnaya ekonomika. – 2010. - #2 – P. 62-67.

Google Scholar

[6] Momot M.V., Nesteruk D.N. Gathering and processing the expert data using web-centric system / Gornoe mashinostroenie: Sbornik materialov. Otdel'nyi vypusk Gornogo informacionno-analiticheskogo byulletenya (nauchno-tehnicheskogo zhurnala). – 2011. - #OV2. – 488p. – M.: izdatel'stvo «Gornaya kniga». P. 330-335.

Google Scholar

[7] Korikov A.M., Nesteruk D.N. Estimation of innovative projects efficiency in mechanical engineering / Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, #2 (24), v. 3, 2011, P. 142-147.

Google Scholar

[8] U. S. Environmental Protection Agency. 1998. Guidelines for Ecological Risk Assessment. Published on May 14, 1998, Federal Register 63(93): 26846-26924. Washington, D.C.: U. S. Environmental Protection Agency.

Google Scholar

[9] J. Scott Armstrong. The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy / International Journal of Applied Forecasting, Vol. 1, pp.29-35, June (2005).

Google Scholar

[10] Rothwell R. Towards the fifth-generation innovation process / International Marketing Review, Vol. 11 No. 1, 1994. MCB University Press, pp.7-31.

Google Scholar