nach oben

Neural Computing and Applications

Erschienen in:

12.11.2020 | S.I. : SPIoT 2020

Exploring resource management for innovation power network based on deep learning algorithm

verfasst von: Congmin Liu, Xiaojing Zheng

Erschienen in: Neural Computing and Applications | Ausgabe 9/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Innovation network is an autocatalytic network; however, there are several factors that affect the process and result. In the beginning, the innovation is very difficult due to certain blocked factors. Based on the machine learning network resource fusion algorithm, this paper conducts research on the innovation process and innovation driving force based on autocatalytic network. An autocatalytic network is introduced to explain the innovation process, including the evolutionary mechanism, evolution law, and the critical property of system innovation. It is concluded that the key of innovation is to unlock some key effect factors in core or find the key factors that connect different innovation. At last, the evolution law and the critical point are given, which could make us make sound and timely decisions.

Vorheriger Artikel Cross-view similarity exploration for unsupervised cross-domain person re-identification

Nächster Artikel Overflow warning and remote monitoring technology based on improved random forest

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Drobyazko S, Hryhoruk I, Pavlova H et al (2019) Entrepreneurship innovation model for telecommunications enterprises. J Entrepreneur Educ 22(2):1–6

Villarreal O, Calvo N (2015) From the Triple Helix model to the Global Open Innovation model: a case study based on international cooperation for innovation in Dominican Republic. J Eng Tech Manage 35:71–92CrossRef

Barbieri JC, Álvares ACT (2016) Sixth generation innovation model: description of a success model. RAI Revista de Administração e Inovação 13(2):116–127CrossRef

Kim S, Son C, Yoon B et al (2015) Development of an innovation model based on a service-oriented product service system (PSS). Sustainability 7(11):14427–14449CrossRef

Pashaeypoor S, Ashktorab T, Rassouli M et al (2016) Predicting the adoption of evidence-based practice using “Rogers diffusion of innovation model.” Contemp nurse 52(1):85–94CrossRef

Howell BL, Conway PH, Rajkumar R (2015) Guiding principles for center for medicare & medicaid innovation model evaluations. JAMA 313(23):2317–2318CrossRef

Chen CK, Shie AJ, Wang KM et al (2015) An ageing-in-place service innovation model by using TRIZ methodology. Human Fact Ergonom Manufact Serv Ind 25(2):166–182CrossRef

Khomsi MR (2016) The smart city ecosystem as an innovation model: lessons from Montreal. Technol Innov Manag Rev 6(11):26–31CrossRef

Fabrício RS Jr, da Silva FR, Simões E et al (2015) Strengthening of open innovation model: using startups and technology parks. IFAC-PapersOnLine 48(3):14–20CrossRef

10.

Radnejad AB, Vredenburg H, Woiceshyn J (2017) Meta-organizing for open innovation under environmental and social pressures in the oil industry. Technovation 66:14–27CrossRef

11.

Carayannis EG, Sindakis S, Walter C (2015) Business model innovation as lever of organizational sustainability. J Technol Transfer 40(1):85–104CrossRef

12.

Linder M, Williander M (2017) Circular business model innovation: inherent uncertainties. Bus Strategy Environ 26(2):182–196CrossRef

13.

Au TD (2016) Using open innovation model to enhance knowledge sharing in Vietnam University. J Administr Bus Stud 2(5):241–247

14.

Visnjic I, Wiengarten F, Neely A (2016) Only the brave: Product innovation, service business model innovation, and their impact on performance. J Prod Innov Manag 33(1):36–52CrossRef

15.

Christensen CM, Bartman T, Van Bever D (2016) The hard truth about business model innovation. MIT Sloan Manage Rev 58(1):31

16.

Rayna T, Striukova L (2016) From rapid prototyping to home fabrication: How 3D printing is changing business model innovation. Technol Forecast Soc Chang 102:214–224CrossRef

17.

Parker G, Van Alstyne M (2018) Innovation, openness, and platform control. Manage Sci 64(7):3015–3032CrossRef

18.

Christensen CM, Raynor ME, McDonald R (2015) What is disruptive innovation. Harvard Bus Rev 93(12):44–53

19.

Amabile TM, Pratt MG (2016) The dynamic componential model of creativity and innovation in organizations: making progress, making meaning. Res Organ Behav 36:157–183

20.

Clauss T (2017) Measuring business model innovation: conceptualization, scale development, and proof of performance. R&D Manage 47(3):385–403CrossRef

21.

Martins LL, Rindova VP, Greenbaum BE (2015) Unlocking the hidden value of concepts: a cognitive approach to business model innovation. Strat Entrepreneur J 9(1):99–117CrossRef

22.

Ginting G (2015) Open innovation model: empowering entrepreneurial orientation and utilizing network resources as determinant for internationalization performance of small medium agroindustry. Agri Agri Sci Proc 3:56–61

23.

Foss NJ, Saebi T (2017) Fifteen years of research on business model innovation: How far have we come, and where should we go? J Manag 43(1):200–227

24.

França CL, Broman G, Robert KH et al (2017) An approach to business model innovation and design for strategic sustainable development. J Clean Prod 140:155–166CrossRef

25.

Evans S, Vladimirova D, Holgado M et al (2017) Business model innovation for sustainability: towards a unified perspective for creation of sustainable business models. Bus Strat Environ 26(5):597–608CrossRef

Titel: Exploring resource management for innovation power network based on deep learning algorithm
verfasst von: Congmin Liu
Xiaojing Zheng
Publikationsdatum: 12.11.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05446-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 9/2021

Modeling fractional polytropic gas spheres using artificial neural network

Source localization in resource-constrained sensor networks based on deep learning

Two-stage adaptive integration of multi-source heterogeneous data based on an improved random subspace and prediction of default risk of microcredit

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

Application of the group method of data handling and variable importance analysis for prediction and modelling of saltwater intrusion processes in coastal aquifers

Joint deep separable convolution network and border regression reinforcement for object detection

Premium Partner