nach oben

Soft Computing

Erschienen in:

15.10.2016 | Methodologies and Application

Adaptive neural model optimized by modified differential evolution for identifying 5-DOF robot manipulator dynamic system

verfasst von: Nguyen Ngoc Son, Ho Pham Huy Anh, Truong Dinh Chau

Erschienen in: Soft Computing | Ausgabe 3/2018

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

This paper proposes an adaptive neural network (ANN) model for modeling the nonlinear dynamic of a robot manipulator based on experimental input–output data from the system. The ANN model is created by combining the multilayer perceptron neural network structure and the nonlinear auto-regressive with eXogenous input model and is trained by the modified differential evolution (MDE) algorithm. The effectiveness of the proposed method is evaluated and compared with other algorithms such as the back-propagation algorithm, the traditional differential evolution and the hybrid differential evolution–back-propagation algorithm. The results prove that the proposed ANN model optimized by the MDE algorithm for the 5-DOF robot manipulator dynamic is successfully modeled and performed well.

Vorheriger Artikel A model for providing emotion awareness and feedback using fuzzy logic in online learning

Nächster Artikel Picture 2-tuple linguistic aggregation operators in multiple attribute decision making

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

Anh HPH, Ahn KK (2013) A new approach of the 2-axes pam robot arm identification using neural mimo narx model. Int J Artif Intel Tools 22(01):1–26CrossRef

Basso M, Giarre L, Groppi S, Zappa G (2005) Narx models of an industrial power plant gas turbine. Control Syst Technol IEEE Trans 13(4):599–604CrossRef

Brest J, Bošković B, Greiner S, Žumer V, Maučec MS (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput 11(7):617–629CrossRefMATH

Chauhan N, Ravi V, Chandra DK (2009) Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst Appl 36(4):7659–7665CrossRef

Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. Evolut Comput IEEE Trans 13(3):526–553CrossRef

Das H, Jena AK, Nayak J, Naik B, Behera H (2015) A novel pso based back propagation learning-mlp (pso-bp-mlp) for classification. Computational intelligence in data mining, vol 2. Springer, Berlin, pp 461–471

Dash CSK, Dehuri S, Cho S-B, Wang G-N (2015) Towards crafting a smooth and accurate functional link artificial neural networks based on differential evolution and feature selection for noisy database. Int J Comput Intel Syst 8(3):539–552CrossRef

Diaconescu E (2008) The use of narx neural networks to predict chaotic time series. Wseas Trans Comput Res 3(3):182–191

Gämperle R, Müller SD, Koumoutsakos P (2002) A parameter study for differential evolution. Adv Intel Syst Fuzzy Syst Evol Comput 10:293–298

Gaspar CRM, Velázquez-Velázquez JE, Rodríguez JCT, (2015) Identification of a cylindrical robot using recurrent neural networks. Multibody mechatronic systems, pp 381–389. Springer, Berlin

Gori M, Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intel 1:76–86CrossRef

Hovland G, Berglund E, Hanssen S, AB AR (2001) Identification of coupled elastic dynamics using inverse eigenvalue theory. In: International symposium on robotics, vol 19, p 21

Huang H-X, Li J-C, Xiao C-L (2015) A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm. Exp Syst Appl 42(1):146–155CrossRef

Hung Y-C, Lin F-J, Hwang J-C, Chang J-K, Ruan K-C (2015) Wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution. Power Electr IEEE Trans 30(4):2350–2362CrossRef

Ilonen J, Kamarainen J-K, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17(1):93–105CrossRef

Karaboga N, Cetinkaya B, (2005) Performance comparison of genetic and differential evolution algorithms for digital fir filter design. Advances in information systems, pp 482–488. Springer, Berlin

Kuschewski JG, Hui S et al (1993) Application of feedforward neural networks to dynamical system identification and control. Control Syst Technol IEEE Trans 1(1):37–49CrossRef

Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462CrossRefMATH

Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. Neural Netw IEEE Trans 1(1):4–27CrossRef

Östring M, Gunnarsson S, Norrlöf M (2003) Closed-loop identification of an industrial robot containing flexibilities. Control Eng Pract 11(3):291–300CrossRef

Pham MT, Gautier M, Poignet P (2001) Identification of joint stiffness with bandpass filtering. In: Robotics and automation, IEEE international conference on, vol 3, IEEE (2001), pp 2867–2872

Piotrowski AP (2014) Differential evolution algorithms applied to neural network training suffer from stagnation. Appl Soft Comput 21:382–406CrossRef

Piroddi L, Spinelli W (2003) An identification algorithm for polynomial narx models based on simulation error minimization. Int J Control 76(17):1767–1781MathSciNetCrossRefMATH

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. Evol Comput IEEE Trans 13(2):398–417CrossRef

Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. Proc IEEE CEC 1:506–513

Sarangi PP, Sahu A, Panda M (2013) A hybrid differential evolution and back-propagation algorithm for feedforward neural network training. Int J Comput Appl 84(14):1–9

Son NN, Anh HPH (2015) Adaptive displacement online control of shape memory alloys actuator based on neural networks and hybrid differential evolution algorithm. Neurocomputing 166:464–474CrossRef

Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefMATH

Subudhi B, Jena D (2011) A differential evolution based neural network approach to nonlinear system identification. Appl Soft Comput 11(1):861–871CrossRef

Subudhi B, Jena D (2011) Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing 74(10):1696–1709CrossRef

Swevers J, Verdonck W, Schutter JD (2007) Dynamic model identification for industrial robots. Control Syst IEEE 27(5):58–71MathSciNetCrossRef

Vesterstrøm J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Evolutionary computation, congress on, vol 2, IEEE, pp 1980–1987

Wang X, Tang Z, Tamura H, Ishii M, Sun W (2004) An improved backpropagation algorithm to avoid the local minima problem. Neurocomputing 56:455–460CrossRef

Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. Evol Comput IEEE Trans 15(1):55–66CrossRef

Titel: Adaptive neural model optimized by modified differential evolution for identifying 5-DOF robot manipulator dynamic system
verfasst von: Nguyen Ngoc Son
Ho Pham Huy Anh
Truong Dinh Chau
Publikationsdatum: 15.10.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2401-x

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 3/2018

A secure transaction of cloud data using conditional source trust attributes encryption mechanism

A model for providing emotion awareness and feedback using fuzzy logic in online learning

Session-aware music recommendation via a generative model approach

Combining a continuous location model and Heuristic techniques to determine oilfield warehouse locations under future oil well location uncertainty

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

A new portfolio selection model with interval-typed random variables and the empirical analysis

Premium Partner