Skip to main content
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2017

11.12.2015 | Original Article

An optimal learning-based controller derived from Hamiltonian function combined with a cellular searching strategy for automotive coldstart emissions

verfasst von: Nasser L. Azad, Ahmad Mozaffari, Alireza Fathi

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2017

Einloggen

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

search-config
loading …

Abstract

The main objective of this study is to propose a novel algorithmic framework for the implementation of a nonlinear controller for reducing the amount of tailpipe hydrocarbon emissions in automotive engines over the coldstart period. To this aim, the control problem for a given engine is formulated in the form of the standard Bolza problem, and then, the concepts of Euler–Lagrange equation and Hamiltonian function are taken into account to calculate the optimal states, co-states, and control input signals. An extreme learning machine is also linked to an experimentally validated nonlinear state-space representation of the engine during the coldstart to approximate the values of exhaust gas temperature and engine-out hydrocarbon emissions, which are two key variables for the considered control problem. To solve the resulting system of equations, a cellular variant of the particle swarm optimization technique is implemented and the existing nonlinear system of equations is solved heuristically. In addition, some constraints are exerted on the control signals to guarantee the smooth operation of the engine by applying the calculated controlling commands. Finally, the authenticity of the resulting optimal controller is validated against a classical Pontryagin’s minimum principle-based control system. Generally, the findings demonstrate the effectiveness of the proposed control methodology to reduce coldstart hydrocarbon emissions in automotive engines.

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!

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!

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!

Weitere Produktempfehlungen anzeigen
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Zurück zum Zitat Rajmani R (2012) Vehicle dynamics and control. Springer-Verlag, Mech Eng SeriesCrossRef Rajmani R (2012) Vehicle dynamics and control. Springer-Verlag, Mech Eng SeriesCrossRef
3.
Zurück zum Zitat Ulsoy AG, Peng H, Cakmakci M (2014) Automotive control systems. Cambridge Press, Cambridge Ulsoy AG, Peng H, Cakmakci M (2014) Automotive control systems. Cambridge Press, Cambridge
4.
Zurück zum Zitat Zhai YJ, Yu DL (2009) Neural network model-based automotive engine air/fuel ratio control and robustness evaluation. Eng Appl Artif Intell 22:171–180CrossRef Zhai YJ, Yu DL (2009) Neural network model-based automotive engine air/fuel ratio control and robustness evaluation. Eng Appl Artif Intell 22:171–180CrossRef
5.
Zurück zum Zitat Taghavipour A, Azad NL, McPhee J (2012) An optimal power management strategy for power-split plug-in hybrid electric vehicles. Int J Veh Des 60(3/4):286–304CrossRef Taghavipour A, Azad NL, McPhee J (2012) An optimal power management strategy for power-split plug-in hybrid electric vehicles. Int J Veh Des 60(3/4):286–304CrossRef
6.
Zurück zum Zitat Asadi B, Vahidi A (2011) Predictive cruise control: utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Trans Control Syst Technol 19(3):707–714CrossRef Asadi B, Vahidi A (2011) Predictive cruise control: utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Trans Control Syst Technol 19(3):707–714CrossRef
7.
Zurück zum Zitat Xiao L, Gao F (2010) A comprehensive review of the development of adaptive cruise control systems. Veh Syst Dyn 48(10):1167–1192CrossRef Xiao L, Gao F (2010) A comprehensive review of the development of adaptive cruise control systems. Veh Syst Dyn 48(10):1167–1192CrossRef
8.
Zurück zum Zitat Azad NL, Sanketi PR, Hedrick JK (2012) Determining model accuracy requirements for automotive engine coldstart hydrocarbon emissions control. J Dyn Syst T ASME 134(5):051002CrossRef Azad NL, Sanketi PR, Hedrick JK (2012) Determining model accuracy requirements for automotive engine coldstart hydrocarbon emissions control. J Dyn Syst T ASME 134(5):051002CrossRef
9.
Zurück zum Zitat Vajedi M, Azad NL (2014) Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Trans Intell Trans Syst. doi:10.1109/TITS.2015.2462843 Vajedi M, Azad NL (2014) Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Trans Intell Trans Syst. doi:10.​1109/​TITS.​2015.​2462843
10.
Zurück zum Zitat Qin G, Ge A, Lee JJ (2006) Fuzzy logic control for automobiles I: knowledge-based gear position decision. Adv Ind, Control, pp 145–157 Qin G, Ge A, Lee JJ (2006) Fuzzy logic control for automobiles I: knowledge-based gear position decision. Adv Ind, Control, pp 145–157
11.
Zurück zum Zitat Chen X, Wang Y, Haskara I, Zhu G (2014) Optimal air-to-fuel ratio tracking control with adaptive biofuel content estimation for LNT regeneration. IEEE Trans Contr Sys Tech 22(2):428–439CrossRef Chen X, Wang Y, Haskara I, Zhu G (2014) Optimal air-to-fuel ratio tracking control with adaptive biofuel content estimation for LNT regeneration. IEEE Trans Contr Sys Tech 22(2):428–439CrossRef
12.
Zurück zum Zitat Cheng X, Jiang S, Wang S (2011) Design of a sliding mode controller for automotive engine speed regulation. In: IEEE Conference Industrial Electronics and Applications, Beijing, pp 1722–1725 Cheng X, Jiang S, Wang S (2011) Design of a sliding mode controller for automotive engine speed regulation. In: IEEE Conference Industrial Electronics and Applications, Beijing, pp 1722–1725
13.
Zurück zum Zitat Mozaffari A, Vajedi M, Azad NL (2015) A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor. Neurocomputing 151(2):845–856CrossRef Mozaffari A, Vajedi M, Azad NL (2015) A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor. Neurocomputing 151(2):845–856CrossRef
14.
Zurück zum Zitat Salehi R, Shahbakhti M, Hedrick JK (2014) Real-time hybrid switching control of automotive cold start hydrocarbon emission. J Dyn Syst T ASME 136:041002CrossRef Salehi R, Shahbakhti M, Hedrick JK (2014) Real-time hybrid switching control of automotive cold start hydrocarbon emission. J Dyn Syst T ASME 136:041002CrossRef
15.
Zurück zum Zitat Dextreit C, Kolmonovsky IV (2014) Game theory controller for hybrid electric vehicles. IEEE Trans Contr Sys Tech 22(2):652–663CrossRef Dextreit C, Kolmonovsky IV (2014) Game theory controller for hybrid electric vehicles. IEEE Trans Contr Sys Tech 22(2):652–663CrossRef
16.
Zurück zum Zitat Azad NL, Khajepour A, McPhee J (2007) Robust state feedback stabilization of articulated steer vehicles. Vehicle Syst Dyn 45(3):249–275CrossRef Azad NL, Khajepour A, McPhee J (2007) Robust state feedback stabilization of articulated steer vehicles. Vehicle Syst Dyn 45(3):249–275CrossRef
17.
Zurück zum Zitat Zhang S, Zhang C, Han G, Wang Q (2014) Optimal control strategy design based on dynamic programming for a dual-motor coupling-propulsion system, Sci World J 2014: Article ID: 958239 Zhang S, Zhang C, Han G, Wang Q (2014) Optimal control strategy design based on dynamic programming for a dual-motor coupling-propulsion system, Sci World J 2014: Article ID: 958239
18.
Zurück zum Zitat Prokhorov DV (2008) Computational intelligence in automotive applications. Studies in Computational Intelligence. Springer-Verlag, BerlinCrossRef Prokhorov DV (2008) Computational intelligence in automotive applications. Studies in Computational Intelligence. Springer-Verlag, BerlinCrossRef
19.
Zurück zum Zitat Zavala JC (2007) Engine modeling and control for minimization of hydrocarbon coldstart emissions in SI engine. Ph.D. Thesis, University of California, Berkeley, USA Zavala JC (2007) Engine modeling and control for minimization of hydrocarbon coldstart emissions in SI engine. Ph.D. Thesis, University of California, Berkeley, USA
20.
Zurück zum Zitat Sanketi PR, Zavala JC, Hedrick JK (2006) Automotive engine hybrid modeling and control for reduction of hydrocarbon emissions. Int J Control 79(5):449–464CrossRefMATH Sanketi PR, Zavala JC, Hedrick JK (2006) Automotive engine hybrid modeling and control for reduction of hydrocarbon emissions. Int J Control 79(5):449–464CrossRefMATH
21.
Zurück zum Zitat Wittka T, Muller V, Dittmann P, Pischinger S (2015) Development and investigation of diesel fuel reformer for LNT regeneration. Emiss Control Sci Technol. doi:10.1007/s40825-015-0017-8 Wittka T, Muller V, Dittmann P, Pischinger S (2015) Development and investigation of diesel fuel reformer for LNT regeneration. Emiss Control Sci Technol. doi:10.​1007/​s40825-015-0017-8
22.
Zurück zum Zitat Brijesh P, Sreedhara S (2013) Exhaust emissions and its control methods in compression ignition engines: a review. Int J Auto Tech-Kor 14(2):195–206CrossRef Brijesh P, Sreedhara S (2013) Exhaust emissions and its control methods in compression ignition engines: a review. Int J Auto Tech-Kor 14(2):195–206CrossRef
23.
Zurück zum Zitat Shaw B, Hedrick JK (2003) Closed-loop engine coldstart control to reduce hydrocarbon emissions, American Control Conference 1392–1397 Shaw B, Hedrick JK (2003) Closed-loop engine coldstart control to reduce hydrocarbon emissions, American Control Conference 1392–1397
24.
Zurück zum Zitat Sanketi PR, Zavala JC, Wilcutts M, Kaga T, Hedrick JK (2007) MIMO control for automotive coldstart. Fifth IFAC Symposium on Advances in Automotive Control, August Sanketi PR, Zavala JC, Wilcutts M, Kaga T, Hedrick JK (2007) MIMO control for automotive coldstart. Fifth IFAC Symposium on Advances in Automotive Control, August
25.
Zurück zum Zitat Zavala JC, Sanketi PR, Wilcutts M, Kaga T, Hedrick JK (2007) Simplified models of engine HC emissions, exhaust temperature and catalyst temperature for automotive coldstart, Fifth IFAC Symposium on Advances in Automotive Control, August Zavala JC, Sanketi PR, Wilcutts M, Kaga T, Hedrick JK (2007) Simplified models of engine HC emissions, exhaust temperature and catalyst temperature for automotive coldstart, Fifth IFAC Symposium on Advances in Automotive Control, August
26.
Zurück zum Zitat Mozaffari A, Azad NL (2014) Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification. Neurocomputing 131:143–156CrossRef Mozaffari A, Azad NL (2014) Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification. Neurocomputing 131:143–156CrossRef
27.
Zurück zum Zitat Mozaffari A, Azad NL (2014) A robust time delay auto-regressive exogenous fuzzy inference system for real-time estimation of catalyst temperature over engines coldstart operation: a multiobjective implementation scenario. Int J Dyn Control. doi:10.1007/s40435-014-0133-2 Mozaffari A, Azad NL (2014) A robust time delay auto-regressive exogenous fuzzy inference system for real-time estimation of catalyst temperature over engines coldstart operation: a multiobjective implementation scenario. Int J Dyn Control. doi:10.​1007/​s40435-014-0133-2
28.
Zurück zum Zitat Mozaffari A, Azad NL (2015) Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission. J Exp Theor Artif Intell 27(3):253–272CrossRef Mozaffari A, Azad NL (2015) Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission. J Exp Theor Artif Intell 27(3):253–272CrossRef
29.
Zurück zum Zitat Naidu DS (2003) Optimal control systems. CRC Press Naidu DS (2003) Optimal control systems. CRC Press
30.
Zurück zum Zitat Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107–122CrossRef Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107–122CrossRef
31.
Zurück zum Zitat Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning: data mining, inference, and prediction, Springer Series in Statistics Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning: data mining, inference, and prediction, Springer Series in Statistics
33.
Zurück zum Zitat Fathi A, Mozaffari A (2014) Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature. Appl Soft Comput 14:229–251CrossRef Fathi A, Mozaffari A (2014) Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature. Appl Soft Comput 14:229–251CrossRef
34.
Zurück zum Zitat Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRef Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRef
35.
Zurück zum Zitat Mozaffari A, Behzadipour S (2015) A modular extreme learning machine with linguistic interpreter and accelerated chaotic distributor for evaluating the safety of robot maneuvers in laparoscopic surgery. Neurocomputing 151(2):913–932CrossRef Mozaffari A, Behzadipour S (2015) A modular extreme learning machine with linguistic interpreter and accelerated chaotic distributor for evaluating the safety of robot maneuvers in laparoscopic surgery. Neurocomputing 151(2):913–932CrossRef
36.
Zurück zum Zitat Emami M, Mozaffari A, Azad NL, Rezaie B (2014) An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes. Int J Comput Math. doi:10.1080/00207160.2014.985664 Emami M, Mozaffari A, Azad NL, Rezaie B (2014) An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes. Int J Comput Math. doi:10.​1080/​00207160.​2014.​985664
37.
Zurück zum Zitat Mozaffari A, Emami M, Azad NL, Fathi A (2014) On the efficacy of chaos-enhanced heuristic walks with nature-based controllers for robust and accurate intelligent search, part A: an experimental analysis. J Exp Theor Artif Intell. doi:10.1080/0952813X.2014.954632 Mozaffari A, Emami M, Azad NL, Fathi A (2014) On the efficacy of chaos-enhanced heuristic walks with nature-based controllers for robust and accurate intelligent search, part A: an experimental analysis. J Exp Theor Artif Intell. doi:10.​1080/​0952813X.​2014.​954632
40.
Zurück zum Zitat Zelinka I, Celikovsky S, Richter H, Chen G (2010) Evolutionary algorithms and chaotic systems, studies in computational intelligence Zelinka I, Celikovsky S, Richter H, Chen G (2010) Evolutionary algorithms and chaotic systems, studies in computational intelligence
41.
Zurück zum Zitat Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. Proc IEEE Int Conf Evol Comput Anchorage Alaska 1998:66–73 Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. Proc IEEE Int Conf Evol Comput Anchorage Alaska 1998:66–73
42.
Zurück zum Zitat Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73CrossRef Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73CrossRef
43.
Zurück zum Zitat Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm optimization scheme, Lecture Series on Computational Sciences, pp 868–873 Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm optimization scheme, Lecture Series on Computational Sciences, pp 868–873
44.
Zurück zum Zitat Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8:204–210CrossRef Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8:204–210CrossRef
45.
Zurück zum Zitat Peram T, Veeramachaneni K, Mohan CK (2003) Fitness–distance-ratio based particle swarm optimization, Proceedings of Swarm Intelligence Symposium, pp 174–181 Peram T, Veeramachaneni K, Mohan CK (2003) Fitness–distance-ratio based particle swarm optimization, Proceedings of Swarm Intelligence Symposium, pp 174–181
46.
Zurück zum Zitat Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRef Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRef
47.
Zurück zum Zitat Azad NL (2015) On-line optimization of automotive engine coldstart hydrocarbon emissions control at idle conditions. Proc IMechE I J Sys Contr Eng 229(9):781–796CrossRef Azad NL (2015) On-line optimization of automotive engine coldstart hydrocarbon emissions control at idle conditions. Proc IMechE I J Sys Contr Eng 229(9):781–796CrossRef
49.
Zurück zum Zitat Wong PK, Wong HC, Vong CM, Long TM, Wong KI, Gao X (2015) Fault tolerance automotive air-ratio control using extreme learning machine model predictive controller, Math Probl Eng 2015, Article ID: 317142 Wong PK, Wong HC, Vong CM, Long TM, Wong KI, Gao X (2015) Fault tolerance automotive air-ratio control using extreme learning machine model predictive controller, Math Probl Eng 2015, Article ID: 317142
50.
Zurück zum Zitat Vaughan A, Bohac SV (2015) Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series. Neural Netw 70:18–26CrossRef Vaughan A, Bohac SV (2015) Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series. Neural Netw 70:18–26CrossRef
51.
Zurück zum Zitat Janakiraman VM, Nguyen XL, Sterniak J, Assanis D (2015) Identification of the dynamic operating envelope of HCCI engines using class imbalance learning. IEEE Trans Neural Netw Learn Syst 26(1):98–112MathSciNetCrossRef Janakiraman VM, Nguyen XL, Sterniak J, Assanis D (2015) Identification of the dynamic operating envelope of HCCI engines using class imbalance learning. IEEE Trans Neural Netw Learn Syst 26(1):98–112MathSciNetCrossRef
52.
Zurück zum Zitat Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energ 74:640–647CrossRef Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energ 74:640–647CrossRef
53.
Zurück zum Zitat Wong KI, Vong CM, Wong PK, Luo J (2015) Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction. Neurocomputing 149:397–404CrossRef Wong KI, Vong CM, Wong PK, Luo J (2015) Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction. Neurocomputing 149:397–404CrossRef
54.
Zurück zum Zitat Wong PK, Vong CM, Gao XH, Wong KI (2014) Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel Ratio regulation, Math Probl Eng 2014, Article ID: 246964 Wong PK, Vong CM, Gao XH, Wong KI (2014) Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel Ratio regulation, Math Probl Eng 2014, Article ID: 246964
55.
Zurück zum Zitat Mozaffari A, Azad NL, Hedrick JK (2015) A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction. Neurocomputing. doi:10.1016/j.neucom.2015.08.070 Mozaffari A, Azad NL, Hedrick JK (2015) A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction. Neurocomputing. doi:10.​1016/​j.​neucom.​2015.​08.​070
56.
Zurück zum Zitat Wong PK, Wong HC, Vong CM, Xie Z, Huang S (2015) Model predictive engine air-ratio control using online sequential extreme learning machine. Neural Comput Appl. doi:10.1007/s00521-014-1555-7 Wong PK, Wong HC, Vong CM, Xie Z, Huang S (2015) Model predictive engine air-ratio control using online sequential extreme learning machine. Neural Comput Appl. doi:10.​1007/​s00521-014-1555-7
57.
Zurück zum Zitat Shaw B (2002) Modeling and control of automotive coldstart hydrocarbon emissions. Ph.D. Thesis, University of California, Berkeley, USA Shaw B (2002) Modeling and control of automotive coldstart hydrocarbon emissions. Ph.D. Thesis, University of California, Berkeley, USA
Metadaten
Titel
An optimal learning-based controller derived from Hamiltonian function combined with a cellular searching strategy for automotive coldstart emissions
verfasst von
Nasser L. Azad
Ahmad Mozaffari
Alireza Fathi
Publikationsdatum
11.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0467-x

Weitere Artikel der Ausgabe 3/2017

International Journal of Machine Learning and Cybernetics 3/2017 Zur Ausgabe

Neuer Inhalt