Skip to main content
SpringerLink
Log in

Evolving Embedded Fuzzy Controllers

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

The interest in research and implementations of type-2 fuzzy controllers (GlossaryTerm

T2FC

s) is increasing. It has been demonstrated that these controllers provide more advantages in handling uncertainties than type-1 FCs (GlossaryTerm

T1FC

s). This characteristic is very appealing because real-world problems are full of inaccurate information from diverse sources. Nowadays, it is no problem to implement an intelligent controller (GlossaryTerm

IC

) for microcomputers since they offer powerful operating systems, high-level languages, microprocessors with several cores, and co-processing capacities on graphic processing units (GlossaryTerm

GPU

s), which are interesting characteristics for the implementation of fast type-2 GlossaryTerm

IC

s (GlossaryTerm

T2IC

s). However, the above benefits are not directly available for the design of embedded GlossaryTerm

IC

s for consumer electronics that need to be implemented in devices such as an application-specific integrated circuit (GlossaryTerm

ASIC

), a field-programmable gate array (GlossaryTerm

FPGA

s), etc. Fortunately, for GlossaryTerm

T1FC

s there are platforms that generate code in GlossaryTerm

VHSIC

hardware description language (GlossaryTerm

VHDL

; GlossaryTerm

VHSIC

: very high speed integrated circuit), C++, and Java. This is not true for the design of GlossaryTerm

T2IC

s, since there are no specialized tools to develop the inference system as well as to optimize it.

The aim of this chapter is to present different ways of achieving high-performance computing for evolving GlossaryTerm

T1

and GlossaryTerm

T2

GlossaryTerm

IC

s embedded into GlossaryTerm

FPGA

s. Therefore, we provide a compiled introduction to GlossaryTerm

T1

and GlossaryTerm

T2

FCs, with emphasis on the well-known bottle neck of the interval GlossaryTerm

T2FC

(GlossaryTerm

IT2FC

), and software and hardware proposals to minimize its effect regarding computational cost. An overview of learning systems and hosting technology for their implementation is given. We explain different ways to achieve such implementations: at the circuit level using a hardware description language, using a multiprocessor system and a high-level language, and combining both methods. We explain how to use the GlossaryTerm

IT2FC

developed in GlossaryTerm

VHDL

as a standalone system, and as a coprocessor for the GlossaryTerm

FPGA

Fusion of Actel, Spartan 6, and Virtex 5. We present the methodology and two new proposals to achieve evolution of the GlossaryTerm

IT2FC

for GlossaryTerm

FPGA

, one for the static region of the GlossaryTerm

FPGA

, and the other one for the reconfigurable region using the dynamic partial reconfiguration methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 269.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 349.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ADC:

analog digital converter

ALU:

arithmetic unit

API:

application programming interface

ASIC:

application-specific integrated circuit

BSB:

base system builder

CF:

compact flash

CLB:

configurable logic block

clk:

clock

CMOS:

complementary metal-oxide-semiconductor

COGIN:

coverage-based genetic induction

CR:

control register

CUDA:

compute unified device architecture

CW:

control word

DC/AD:

change/activate-deactivate

DPR:

dynamic partial reconfiguration

DSP:

digital signal processing

DW:

data word

EA:

evolutionary algorithm

EAPR:

early access partial reconfiguration

EFRBS:

evolutionary FRBS

EKMANI:

enhanced Karnik–Mendel algorithm with new initialization

EKM:

enhanced KM

EODS:

enhanced opposite directions searching

ES:

embedding system

FIS:

fuzzy inference system

FlexCo:

flexible coprocessor

FL:

fuzzy logic

FOU:

footprint of uncertainty

FPGA:

field programmable gate array

FRBS:

fuzzy rule-based system

FS:

fuzzy set

GA:

genetic algorithm

GPGPU:

general-purpose GPU

GPIO:

general-purpose input/output interface

GPU:

graphics processing unit

GT2:

generalized T2FS

HDL:

hardware description language

HPC:

high-performance computing

HW:

hardware

HWICAP:

hardware internal configuration access point

IASC:

iterative algorithm with stop condition

ICAP:

internal configuration access point

IC:

intelligent controller

interrupt controller

IE:

inference engine

IOB:

input/output block

IPIF:

intellectual property interface

IP:

intellectual property

ISE:

Integrated Synthesis Environment

IT2FC:

interval T2FC

IT2FS:

interval T2FS

IT2:

interval type-2

KM:

Karnik–Mendel

LB:

logic block

LT:

linguistic term

LUT:

look-up table

LV:

linguistic variable

LVT:

linguistic-variable-term

MF:

membership function

MPS:

multiprocessor system

OPB:

on-chip peripheral bus

PAR:

place and route

PD:

proportional-differential

PLA:

programmable logic array

PLB:

processor local bus

PRM:

partially reconfigurable module

PR:

partial reconfiguration

PRR:

partially reconfigurable region

RAM:

random access memory

REGAL:

relational genetic algorithm learner

RGN:

random generation number

RISC:

reduced intstruction set computer

ROM:

read only memory

rst:

reset

RTL:

register transfer logic

RT:

real-time

S-bit:

section-bit

SPR:

static partial reconfiguration

SW:

software

T1FC:

type-1 fuzzy controller

T1FS:

type-1 fuzzy set

T1:

type-1

T2FC:

type-2 fuzzy controller

T2FS:

type-2 fuzzy set

T2IC:

type-2 intelligent controller

T2MF:

type-2 membership function

T2:

type-2

TR:

type reducer

UART:

universal asynchronous receiver/transmitter

UCF:

user constraint file

VHDL:

VHSIC hardware description language

VHSIC:

very high speed integrated circuit

WT:

Wu–Tan

XPS:

Xilinx platform studio

XSG:

Xilinx system generator

References

  1. P.P. Angelov, X. Zhou: Evolving fuzzy-rule-based classifiers from data streams, IEEE Trans. Fuzzy Syst. 16(6), 1462–1475 (2008)

    Article  Google Scholar 

  2. O. Cordón, F. Herrera, F. Hoffman, L. Magdalena: Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (World Scientific, Singapore 2001)

    Book  MATH  Google Scholar 

  3. P. Angelov, R. Buswell: Evolving rule-based models: A tool for intelligent adaptation, IFSA World Congr. 20th NAFIPS Int. Conf. 2001. Jt. 9th, Vancouver, Vol. 2 (2001) pp. 1062–1067

    Google Scholar 

  4. K. De Jong: Learning with genetic algorithms: An overview, Mach. Learn. 3(2), 121–138 (1988)

    Google Scholar 

  5. J.H. Holland: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (MIT Press/Bradford Books, Cambridge 1998)

    Google Scholar 

  6. K.A. De Jong: Evolutionary Computation: A Unified Approach (MIT Press, Cambridge 2006)

    MATH  Google Scholar 

  7. O. Cordón, F. Gomide, F. Herrera, F. Hoffmann, L. Magdalena: Ten years of genetic fuzzy systems: Current framework and new trends, Fuzzy Sets Syst. 141(1), 5–31 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. V. Gilles: SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts, Lect. Notes Comput. Sci. 667, 280–296 (1993)

    Article  Google Scholar 

  9. J. Juan Liu, J. Tin-Yau Kwok: An extended genetic rule induction algorithm, Proc. 2000 Congr. Evol. Comput., Vol. 1 (2000) pp. 458–463

    Google Scholar 

  10. O. Cordón, M.J. del Jesus, F. Herrera, M. Lozano: MOGUL A methodology to obtain genetic fuzzy rule based systems under the iterative rule learning approach, Int. J. Intell. Syst. 14(11), 1123–1153 (1999)

    Article  MATH  Google Scholar 

  11. G.D. Perry, F.S. Stephen: Competition-based induction of decision models from examples, Mach. Learn. 13, 229–257 (1993)

    Article  Google Scholar 

  12. G.D. Perry, F.S. Stephen: Using coverage as a model building constraint in learning classifier systems, Evol. Comput. 2, 67–91 (1994)

    Article  Google Scholar 

  13. A. Giordana, F. Neri: Searc-intensive concept induction, Evol. Comput. 3, 375–416 (1995)

    Article  Google Scholar 

  14. H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka: Selecting fuzzy if-then rules for classification problems using genetic algorithms, IEEE Trans. Fuzzy Syst. 3(3), 260–270 (1995)

    Article  Google Scholar 

  15. A. Homaifar, E. McCormick: Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms, IEEE Trans. Fuzzy Syst. 3(2), 129–139 (1995)

    Article  Google Scholar 

  16. D. Park, A. Kandel, G. Langholz: Genetic-based new fuzzy reasoning models with application to fuzzy control, IEEE Trans. Syst. Man Cybern. 24(1), 39–47 (1994)

    Article  MATH  Google Scholar 

  17. O. Castillo, R. Sepúlveda, P. Melin, O. Montiel: Evolutionary optimization of interval type-2 membership functions, Proc. 2006 Int. Conf. Artif. Intell. ICAI 2006, Las Vegas (2006) pp. 558–564

    Google Scholar 

  18. R. Sepúlveda, O. Castillo, P. Melin, O. Montiel, L.T. Aguilar: Evolutionary optimization of interval type-2 membership functions using the human evolutionary model, FUZZ-IEEE (2007) pp. 1–6

    Google Scholar 

  19. R. Sepúlveda, O. Montiel-Ross, O. Castillo, P. Melin: Optimizing the MFs in type-2 fuzzy logic controllers, using the human evolutionary model, Int. Rev. Autom. Control 3(1), 1–10 (2010)

    Google Scholar 

  20. O. Castillo, P. Melin, A.A. Garza, O. Montiel, R. Sepúlveda: Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms, Soft Comput. 15(6), 1145–1160 (2011)

    Article  Google Scholar 

  21. C. Wagner, H. Hagras: A genetic algorithm based architecture for evolving type-2 fuzzy logic controllers for real world autonomous mobile robots, Fuzzy Syst. Conf, Proc. 2007. FUZZ-IEEE 2007, London (2007) pp. 1–6

    Google Scholar 

  22. J.E. Bonilla, V.H. Grisales, M.A. Melgarejo: Genetic tuned FPGA based PD fuzzy LUT controller, 10th IEEE Int. Conf. Fuzzy Syst. (2001) pp. 1084–1087

    Google Scholar 

  23. S. Sánchez-Solano, A.J. Cabrera, I. Baturone: FPGA implementation of embedded fuzzy controllers for robotic applications, IEEE Trans. Ind. Electron. 54(4), 1937–1945 (2007)

    Article  Google Scholar 

  24. J.L. González, O. Castillo, L.T. Aguilar: FPGA as a tool for implementing non-fixed structure fuzzy logic controllers, IEEE Symp. Found. Comput. Intell. 2007. FOCI 2007 (2007) pp. 523–530

    Chapter  Google Scholar 

  25. O. Montiel, Y. Maldonado, R. Sepúlveda, O. Castillo: Simple tuned fuzzy controller embedded into an FPGA, Fuzzy Inf. Proc. Soc. 2008. NAFIPS 2008. Annu. Meet. North Am. (2008) pp. 1–6

    Chapter  Google Scholar 

  26. O. Montiel, J. Olivas, R. Sepúlveda, O. Castillo: Development of an embedded simple tuned fuzzy controller, IEEE Int. Conf. Fuzzy Syst., FUZZ-IEEE 2008, IEEE World Congr. Comput. Intell. (2008) pp. 555–561

    Chapter  Google Scholar 

  27. Y. Maldonado, O. Montiel, R. Sepúlveda, O. Castillo: Design and simulation of the fuzzification stage through the Xilinx system generator. In: Soft Computing for Hybrid Intelligent Systems, Studies in Computational Intelligence, Vol. 154, ed. by O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz (Springer, Berlin, Heidelberg 2008) pp. 297–305

    Chapter  Google Scholar 

  28. J.A. Olivas, R. Sepúlveda, O. Montiel, O. Castillo: Methodology to test and validate a VHDL inference engine through the Xilinx system generator. In: Soft Computing for Hybrid Intelligent Systems, Studies in Computational Intelligence, Vol. 154, ed. by O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz (Springer, Berlin, Heidelberg 2008) pp. 325–331

    Chapter  Google Scholar 

  29. G. Lizárraga, R. Sepúlveda, O. Montiel, O. Castillo: Modeling and simulation of the defuzzification stage using Xilinx system generator and simulink. In: Soft Computing for Hybrid Intelligent Systems, Studies in Computational Intelligence, Vol. 154, ed. by O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz (Springer, Berlin, Heidelberg 2008) pp. 333–343

    Chapter  Google Scholar 

  30. M. Grégory, U. Andres, P. Carlos-Andres, S. Eduardo: A dynamically-reconfigurable FPGA platform for evolving fuzzy systems, Lect. Notes Comput. Sci. 3512, 296–359 (2005)

    Google Scholar 

  31. Y. Maldonado, O. Castillo, P. Melin: Optimization of membership functions for an incremental fuzzy PD control based on genetic algorithms. In: Soft Computing for Intelligent Control and Mobile Robotics, Studies in Computational Intelligence, ed. by O. Castillo, J. Kacprzyk, W. Pedrycz (Springer, Berlin Heidelberg 2011) pp. 195–211

    Google Scholar 

  32. R.M.A. Melgarejo, C.A. Peña-Reyes: Hardware architecture and FPGA implementation of a type-2 fuzzy system, Proc. 14th ACM Great Lakes Symp. VLSI (2004) pp. 458–461

    Google Scholar 

  33. C. Lynch, H. Hagras, V. Callaghan: Parallel type-2 fuzzy logic co-processors for engine management, IEEE Int. Conf. Fuzzy Syst., FUZZ-IEEE (2007) pp. 1–6

    Google Scholar 

  34. R. Sepúlveda, O. Montiel, O. Castillo, P. Melin: Embedding a high speed interval type-2 fuzzy controller for a real plant into an FPGA, Appl. Soft Comput. 12(3), 988–998 (2012)

    Article  Google Scholar 

  35. L.A. Zadeh: Fuzzy sets, Inf. Control 8(3), 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  36. L.A. Zadeh: The concept of a linguistic variable and its application to approximate reasoning -I, Inf. Sci. 8(3), 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  37. J.M. Mendel: Type-2 fuzzy sets: Some questions and answers, IEEE Connect. Newsl. IEEE Neural Netw. Soc. 1, 10–13 (2003)

    Google Scholar 

  38. J.S.R. Jang, C.T. Sun, E. Mizutani: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, Upper Saddle River 1997)

    Google Scholar 

  39. J.M. Mendel: Uncertainty Rule-Based Fuzzy Logic Systems: Introduction and New Directions (Prentice Hall, Upper Saddle River 2001)

    MATH  Google Scholar 

  40. J.M. Mendel: Type-2 fuzzy sets and systems: An overview, IEEE Comput. Intell. Mag. 2(2), 20–29 (2007)

    Article  Google Scholar 

  41. J.M. Mendel, R.I.B. John: Type-2 fuzzy sets made simple, IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)

    Article  Google Scholar 

  42. D. Wu: Approaches for reducing the computational cost of interval type-2 fuzzy logic controllers: overview and comparison, IEEE Trans. Fuzzy Syst. 21(1), 80–99 (2013)

    Article  Google Scholar 

  43. D. Wu: Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Systems: Overview and Comparisons, IEEE Trans. Fuzzy Syst. 21(1), 80–99 (2013)

    Article  Google Scholar 

  44. K. Duran, H. Bernal, M. Melgarejo: Improved iterative algorithm for computing the generalized centroid of an interval type-2 fuzzy set, Fuzzy Inf. Proc. Soc. 2008. NAFIPS 2008. Annu. Meet. North Am. (2008) pp. 1–6

    Chapter  Google Scholar 

  45. D. Wu, M. Nie: Comparison and practical implementation of type reduction algorithms for type-2 fuzzy sets and systems, Proc. IEEE Int. Conf. Fuzzy Syst. (2008) pp. 2131–2138

    Google Scholar 

  46. L.T. Ngo, D.D. Nguyen, L.T. Pham, C.M. Luong: Speed up of interval type-2 fuzzy logic systems based on GPU for robot navigation, Adv. Fuzzy Syst. 2012, 475894 (2012), doi: 10.1155/2012/698062

    Google Scholar 

  47. P. Sundararajan: High Performance Computing Using FPGAs, Xilinx. White Paper: FPGA. WP375 (v1.0), 1–15 (2010)

    Google Scholar 

  48. IBM Redbooks: The Power4 Processor Introduction and Tuning Guide, 1st edn. (IBM, Austin 2001)

    Google Scholar 

  49. J. Yiu: The Definitive Guide To The ARM CORTEX-M0 (Newnes, Oxford 2011)

    Google Scholar 

  50. S.P. Dandamudi: Fundamentals of Computer Organization and Design (Springer, Berlin, Heidelberg 2003)

    MATH  Google Scholar 

  51. T. Tauber, G. Runger: Parallel Programming: For Multicore and Cluster Systems (Springer, Berlin, Heidelberg 2010)

    MATH  Google Scholar 

  52. K.P. Abdulla, M.F. Azeem: A novel programmable CMOS fuzzifiers using voltage-to-current converter circuit, Adv. Fuzzy Syst. 2012, 419370 (2012), doi: 10.1155/2012/419370

    Google Scholar 

  53. D. Fikret, G.Z. Sezgin, P. Banu, C. Ugur: ASIC implementation of fuzzy controllers: A sampled-analog approach, 21st Eur. Solid-State Circuits Conf. 1995 ESSCIRC '95. (1995) pp. 450–453

    Google Scholar 

  54. L. Kourra, Y. Tanaka: Dedicated silicon solutions for fuzzy logic systems, IEE Colloquium on 2 Decades Fuzzy Contr. Part 1 3, 311–312 (1993)

    Google Scholar 

  55. M. Khosla, R.K. Sarin, M. Uddin: Design of an analog CMOS based interval type-2 fuzzy logic controller chip, Int. J. Artif. Intell, Expert Syst. 2(4), 167–183 (2011)

    Google Scholar 

  56. C. Bobda: Introduction to Reconfigurable Computing. Architectures, Algorithms, and Applications (Springer, Berlin, Heidelberg 2007)

    MATH  Google Scholar 

  57. P.C. Pong: FPGA Prototyping by VHDL Examples (Wiley, Hoboken 2008)

    Google Scholar 

  58. M. Oscar, S. Roberto, M. Yazmin, C. Oscar: Design and simulation of the type-2 fuzzification stage: Using active membership functions. In: Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, Studies in Computational Intelligence, Vol. 257, ed. by O. Castillo, W. Pedrycz, J. Kacprzyk (Springer, Berlin, Heidelberg 2009) pp. 273–293

    Chapter  Google Scholar 

  59. S. Roberto, M. Oscar, O. José, C. Oscar: Methodology to test and validate a VHDL inference engine of a type-2 FIS, through the Xilinx system generator. In: Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, Studies in Computational Intelligence, Vol. 257, ed. by O. Castillo, W. Pedrycz, J. Kacprzyk (Springer, Berlin/Heidelberg 2009) pp. 295–308

    Chapter  Google Scholar 

  60. S. Roberto, M.-R. Oscar, C. Oscar, M. Patricia: Embedding a KM type reducer for high speed fuzzy controller into an FPGA. In: Soft Computing in Industrial Applications, Advances in Intelligent and Soft Computing, Vol. 75, ed. by X.-Z. Gao, A. Gaspar-Cunha, M. Köppen, G. Schaefer, J. Wang (Springer, Berlin/Heidelberg 2010) pp. 217–228

    Chapter  Google Scholar 

  61. S. Roberto, M. Oscar, L. Gabriel, C. Oscar: Modeling and simulation of the defuzzification stage of a type-2 fuzzy controller using the Xilinx system generator and simulink. In: Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, Studies in Computational Intelligence, Vol. 257, ed. by O. Castillo, W. Pedrycz, J. Kacprzyk (Springer, Berlin/Heidelberg 2009) pp. 309–325

    Chapter  Google Scholar 

  62. O. Montiel-Ross, J. Quiñones, R. Sepúlveda: Designing high-performance fuzzy controllers combining ip cores and soft processors, Adv. Fuzzy Syst. 2012, 1–11 (2012)

    Article  MATH  Google Scholar 

  63. D.B. Fogel, T. Back: An introduction to evolutionary computation. In: Evolutionary Computation. The Fossile Record, ed. by D.F. Fogel (IEEE, New York 1998)

    Chapter  Google Scholar 

  64. W. Lie, W. Feng-yan: Dynamic partial reconfiguration in FPGAs, 3rd Int. Symp. Intell. Inf. Technol. Appl. (2009) pp. 445–448

    Google Scholar 

  65. D. Lim, M. Peattie: Two Flows for Partial Reconfiguration: Module Based or Small Bit Manipulations. XAPP290. May 17, 2007

    Google Scholar 

  66. Emil Eto: Difference-Based Partial Reconfiguration. XAPP290. December 3, 2007

    Google Scholar 

  67. Xilinx: Early Access Partial Reconfiguration User Guide For ISE 8.1.01i, UG208 (v1.1). May 6, 2012

    Google Scholar 

  68. C.-S. Choi, H. Lee: A self-reconfigurable adaptive FIR filter system on partial reconfiguration platform, IEICE Trans. 90-D(12), 1932–1938 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Av. del Parque No. 131º, B.C. 22414, Mesa de Otay, Tijuana, Mexico

    Oscar H. Montiel Ross & Roberto Sepúlveda Cruz

Authors
  1. Oscar H. Montiel Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Sepúlveda Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oscar H. Montiel Ross .

Editor information

Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Montiel Ross, O.H., Sepúlveda Cruz, R. (2015). Evolving Embedded Fuzzy Controllers. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_76

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43505-2_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation