nach oben

Soft Computing

Erschienen in:

04.01.2021 | Foundations

Fractional-order gradient descent with momentum for RBF neural network-based AIS trajectory restoration

Erschienen in: Soft Computing | Ausgabe 2/2021

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In order to guarantee the integrity and accuracy of ship Automatic Identification System (AIS) data, and prevent maritime traffic accidents and make scientific decisions, a fractional-order gradient with momentum RBF neural network (FOGDM-RBF) is proposed. Fractional-order calculus is applied to gradient descent with momentum algorithm for training neural network. The convergence of the proposed algorithm is proved. The AIS data from Danish and Xiamen port are chosen to test the proposed algorithm. The results show FOGDM-RBF can repair the ship’s AIS trajectories with satisfying learning speed and interpolation accuracy. Comparisons show the proposed algorithm has lower training error than gradient descent, stochastic gradient descent and gradient descent with momentum. Compared with gradient descent, gradient descent with momentum, this algorithm has the advantages of better interpolation performance, higher accuracy, better generalization performance, and is not easy to fall into local optimum.

Vorheriger Artikel A duality for two-sorted lattices

Nächster Artikel Locally -complete and locally complete EMV-algebras

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

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

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

Nur mit Berechtigung zugänglich

Al-Sharif ZA, Al-Saleh MI, Alawneh LM, Jararweh YI (2020) Live forensics of software attacks on cyber-physical systems. Fut Gener Comput Syst 108:1217–1229CrossRef

Din S, Paul A, Ahmad A, Gupta BB, Rho AS (2018) Service orchestration of optimizing continuous features in industrial surveillance using big data based fog-enabled internet of things. IEEE Access 6:21582–21591CrossRef

Dozat T (2016) Incorporating nesterov momentum into adam. Workshop track—ICLR 2016

Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159MathSciNetMATH

Esmaeilbeigi M, Chatrabgoun O, Cheraghi M (2018) Fractional Hermite interpolation using RBFs in high dimensions over irregular domains with application. J Comput Phys 375:1091–1120MathSciNetCrossRef

Feng RZ, Peng S (2018) Quasi-interpolation scheme for arbitrary dimensional scattered data approximation based on natural neighbors and RBF interpolation. J Comput Appl 329:95–105MathSciNetCrossRef

Izquierdo D, Silanes MCLD, Parra MC, Torrens JJ (2014) CS-RBF interpolation of surfaces with vertical faults from scattered data. Math Comput Simul 102:11–23MathSciNetCrossRef

Jahanbakhti H (2020) A novel fractional-order neural network for model reduction of large-scale systems with fractional-order nonlinear structure. Soft Comput. https://doi.org/10.1007/s00500-020-04763-5CrossRef

Kedward L, Allen CB, Rendall TCS (2017) Efficient and exact mesh deformation using multiscale RBF interpolation. J Comput Phys 345:732–751MathSciNetCrossRef

Khan S, Naseem I, Malik MA (2018) A fractional gradient descent-based RBF neural network. Circuits Syst Signal Process 37:5311–5332MathSciNetCrossRef

Khan ZA, Zubair S, Alquhayz H, Azeem M, Ditta AA (2019) Design of momentum fractional stochastic gradient descent for recommender systems. IEEE Access 7:179575–179590CrossRef

Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: International Conference on Learning Representations 2015

Kobayashi M (2017) Gradient descent learning for quaternionic Hopfield neural networks. Neurocomputing 260:174–179CrossRef

Li DM, Deng LB, Gupta BB, Wang HX, Choi C (2019) A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf Sci 479:432–447CrossRef

Liu W, Chen L, Chen YF, Zhang WY (2020) Accelerating federated learning via momentum gradient descent. IEEE Trans Parallel Distrib Syst 31(8):1754–1766CrossRef

Mousavi Y, Alfi A (2018) Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems. Chaos, Solitons Fractals 114:202–215CrossRef

Romani L, Rossini M, Schenone D (2019) Edge detection methods based on RBF interpolation. J Comput Appl Math 349:532–547MathSciNetCrossRef

Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1, Foundations, MIT Press

Stergiou C, Psannis KE (2020) Recent advances delivered in mobile cloud computing's security and management challenges. In: Modern principles, practices, and algorithms for cloud security. https://doi.org/10.4018/978-1-7998-1082-7.ch002

Stergiou C, Psannis KE, Gupta BB, Ishibashi Y (2018) Security, privacy & efficiency of sustainable cloud computing for big data & IoT. Sustain Comput Inform Syst 19:174–184

Sutherland WA (1975) Introduction to Metric and Topological Spaces, vol 23. Oxford University Press, OxfordMATH

Tewari A, Gupta BB (2020) Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework. Fut Gener Comput Syst 108:909–920CrossRef

Utomo D (2017) Stock price prediction using back propagation neural network based on gradient descent with momentum and adaptive learning rate. J Internet Bank Commerce 22(3):1–16MathSciNet

Wang LN, Yang Y, Min RQ, Chakradharb S (2017a) Accelerating deep neural network training with inconsistent stochastic gradient descent. Neural Netw 93:219–229CrossRef

Wang J, Wen YQ, Gou Y, Ye ZY, Chen H (2017b) Fractional-order gradient descent learning of BP neural networks with Caputo derivative. Neural Netw 89:19–30CrossRef

Wu W, Zhang NM, Li ZX, Li L, Liu Y (2008) Convergence of gradient method with momentum for back-propagation neural networks. J Comput Math 26(4):613–623MathSciNetMATH

Yang GL, Zhang BJ, Sang ZY, Wang J, Chen H (2017) A caputo-type fractional-order gradient descent learning of BP neural networks. In: 14th international symposium, ISNN, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26

Yin PH, Zhang S, Lyu JC, Osher S, Qi YY, Xin J (2019) Blended coarse gradient descent for full quantization of deep neural networks. Res Math Sci 6:14MathSciNetCrossRef

Zeiler MD (2012). Adadelta: an adaptive learning rate method. https://arxiv.org/abs/1212.5701

Zhang LY, Zhang PC, Yang J, Li J, Gui ZG (2019) Aperture shape generation based on gradient descent with momentum. IEEE Access 7:157623–157632CrossRef

Titel: Fractional-order gradient descent with momentum for RBF neural network-based AIS trajectory restoration
Publikationsdatum: 04.01.2021
Erschienen in: Soft Computing / Ausgabe 2/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-05484-5

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+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2021

A simple numerical scheme for generation of weighting factors for multiobjective optimisation

Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors

Partial label learning based on label distributions and error-correcting output codes

Whale optimization and sine–cosine optimization algorithms with cellular topology for parameter identification of chaotic systems and Schottky barrier diode models

Locally -complete and locally complete EMV-algebras

An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting

Premium Partner