Bibliography

1

Anderson, J.E.; Rosenfeld, E. (Eds.), Neurocomputing: Foundations of Research, Cambridge (MA): The MIT Press, 1988

2

Arbib, M.a. et.al (eds) The Handbook of Brain Theory and Neural Networks, Cambridge (MA): Bradford Book, 2003 (2nd ed.)

3

Audoin, C.; Guinot, B. The Measurement of TIME. Time, Frequency, and the Atomic Clock, Cambridge (UK): Cambridge University Press, 2001 (Original French Edition 1998)

4

Balzer, W., Empirische Theorien: Modelle, Strukturen, Beispiele, Wiesbaden (Germany): Friedr.Vieweg & Sohn, 1982

5

Balzer, W.; Moulines, C. U.; Sneed, J. D., An Architectonic for Science, Dordrecht (NL): D.Reidel Publishing Company, 1987

6

Baars, J.B.; Gage, N.M. Cognition, Brain, and Consciousness. Introduction to Cognitive Neuroscience, 2nd.ed., Amsterdam et: Elsevier, 2010

7

Berard, B.; Bidoit, M.; Finkel, A., Systems and Software Verification. Model-Checking Techniques and Tools: Model-checking Techniques and Tools,Berlin: Springer, 2001, ( ISBN-10: 3540415238, ISBN-13: 978-3540415237)

8

Bourbaki, N., Theorie Des Ensembles, Paris: Hermann, 1970

9

Bowler, P.J. Evolution. The History of an Idea, Berkeley et al: Univ. of California Press, rev.ed. 1989

10

Burges, Chr.J.C., A Tutorial on Support Vector Machines for Pattern Recognition, In: Data Mining and Knowledge Discovery, 2, 121-167,1998, Boston: Kluwer Academic Publishers

11

Caldarelli, G. Scale-Free Networks. Complex Webs inNature and Technology, Oxford - New York: Oxford University Press, 2007

12

Carnap, R. Logische Syntax der Sprache, Wien: J.Springer, 1934

13

Carnap, R. The logical syntax of language,(Transl. by Amethe Smeaton) London: Paul, Trench, Trubner & Co.,1937

14

Carnap, R. The Logical Syntax of Language, New York: harcourt, Brace, and Company, 1938

15

Carnap, R. The logical syntax of language,(Transl. by Amethe Smeaton, Countess von Zeppelin), 6. impr. (with corr.), London: Routledge & Paul, 1964

16

Carnap, R. The logical syntax of language,(Transl. by Amethe Smeaton), 1st. print., Chicago: La Salle; Ill.: Open Court, 2002

17

Changeux, J.-P., Der neuronale Mensch. Wie die Seele funktioniert - die Entdeckungen der neuen Gehirnforschung, aus dem Französchen von Kober, H., Reinbeck bei Hamburg: Rowohlt, 1984, (ISBN 349800865X)

18

Cristianini, N.; Shawe-Taylor, J.,An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press(ISBN: 0521780195).

19

Damasio, A. The Feeling of What Happens. Body and Emotion in the Making of Consciousness, orlando - Austin - New York: The Harvest Book Harcourt, Inc., 1999

20

Diestel, R. (ed.) Directions in Infinite Graph Theory and Combinatorics. Topics in Discrete Mathematics 3, Publishere: Elsevier - North Holland, 1992, ISBN 0444894144

21

Diestel, R. Graphentheorie, 3rd.rev.ed. Heidelberg: Springer-Verlag, 2006,

22

Dietrich, G.; Stahl, H. Matrizen und Determinanten und ihre Anwendung in Technik und Ökonomie. Thun - Frankfurt am Main: Verlag Harri deutsch, 1978

23

Doeben-Henisch, G.; Bauer-Wersing, U.; Erasmus, L.; Schrader,U.; Wagner, W. Interdisciplinary Engineering of Intelligent Systems. Some Methodological Issues, Proceedings of the International Workshop Modelling Adaptive And Cognitive Systems (ADAPCOG 2008) as part of the Joint Conferences of SBIA'2008 (the 19th Brazilian Symposium on Artificial Intelligence); SBRN'2008 (the 10th Brazilian Symposium on Neural Networks); and JRI'2008 (the Intelligent Robotic Journey) at Salvador (Brazil) Oct-26 - Oct-30, 2008

24

Doeben-Henisch, G.; Wagner, M. Validation within Safety Critical Systems Engineering from a Computational Semiotics Point of View, In: IEEE Africon2007 Intern.Conference, Windhoek (Namibia), Sept.2007

25

Doeben-Henisch, G. Reconstructing Human Intelligence within Computational Semiotics. An Introductory Essay. , In: Loula, A.; Gudwin, J.; Queiroz, J. (Eds). Artificial Cognition Systems, Hershey (PA): Idea Group Inc.,pp.106-139

26

Dudel, J.; Menzel, R.; Schmidt, R.F., Neurowissenschaft. Vom Molekl zur Kognition, 2.Aufl., Berlin: Springer, 2001,(ISBN-10: 3540413359, ISBN-13: 978-3540413356)

27

Eccles, J.C., Die Evolution des Gehirns, die Erschaffung des Selbst, Piper Verlag, (ISBN: 3492237096)

28

Elman, J. L.; Bates, E. A.; Johnson, M. H.; Karmiloff-Smith, A.; Parisi, D.; Plunkett, K., Rethinking Innateness. A connectionist perspective on development, London - Cambridge (MA): The MIT Press, 1996

29

Epigenetic Robotics Website: http://www.epigenetic-robotics.org

30

Farwer, B. $\omega$-Automata, In: Graedel, E.; Thomas, W.; Wilke,Th., (Eds.), Automata, Logics, and Infinite Games. A Guide to Current Research, Berlin: Springer, 2002, (ISBN: 978-3-540-00388-5), Pp.3-21

31

Fraser, J.T. Time - The familiar stranger, 1987, Deutsch: Die Zeit: vertraut und fremd, Basel: Birkhäuser-Verlag, ISBN 3-7643-1990-9

32

Freeman, J.A.; Skapura D.M. Neural Networks. Algorithms, Applications, and Programming Techniques, Reading (MA) - Menlo Park (CA) - New York: Addison-Wesley Publ., 1991

33

Fuchs, N.E., ,Kurs in logischer Programmierung. Wien - New York: Springer Verlag, 1990

34

Gazzaniga, M. S. (Ed.), The Cognitive Neurosciences, London - Cambridge (MA): The MIT Press, 1995

35

F. A. Gers and J. Schmidhuber and F. Cummins Learning to Forget: Continual Prediction with LSTM, Neural Computation, 12(10):2451-2471, 2000.

36

Goerz, G.; Rollinger, C.-R.; Schneeberger, J.; (Eds.) Handbuch der Künstlichen Intelligenz, Mnchen-Wien: Oldenbourg Verlag, 4.Aufl., 2003

37

Goerz, G.; Wachsmuth, I.; Einleitung, in: (Eds.)Goerz, G.; Rollinger, C.-R.; Schneeberger, J.; (Eds.) Handbuch der Künstlichen Intelligenz, Mnchen-Wien: Oldenbourg Verlag, 4.Aufl., 2003, pp.1-16

38

Greenwood, J. A. Review: Maurice Fréchet, Méthode des Fonctions Arbitraires. Théorie des Événements en Chaîne dans le Cas d'un Nombre Fini d'États Possibles. (Traité du Calcul des Probabilités et de ses Applications, vol. 1, no. 3.) Paris, Gauthier-Villars, 1938. 10+315 pp. , In: Bull. Amer. Math. Soc. Volume 45, Number 1 (1939), 56-58.

39

Grossberg, S., Studies of Mind and Brain. Neural Principles of Learning, Perception, Development, Cognition and Motor-Control, Dordrecht (Holland): D.Reidel Publishing Company, 1982

40

Hassoun, M.H. (ed.) Associative Neural Memories. Theory and Implementation. Oxford University Press, Oxford, 1993

41

Haykin, S., Neural Networks. A Cpmprehensive Foundation, 2nd.Ed., Upper Saddle River (NJ): Prentice Hall, 1999

42

Halin, R. Graphentheorie, Vol.1+2, Darmstadt: Wissenschaftliche Buchgesellschaft, 1980, 1981.

43

Hanser, H.(ed.) , Lexikon der Neurowissenschaft. Vol.1-4, Heidelberg - Berlin: Spektrum Akademischer Verlag, 2000-2001

44

Hebb, D.O., The Organization of Behavior: a neuropsychological approach, New York: Wiley, 1949

45

Hilbert, D.; Ackermann, W. Grundzüge der theoretischen Logik, Berlin. J.Springer, 1928

46

S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, In S. C. Kremer and J. F. Kolen, eds., A Field Guide to Dynamical Recurrent Neural Networks. IEEE press, 2001

47

HOPCROFT, H.L.; ULLMAN, J.D. Introduction to Automata Theory, Languages, and Computation, Reading (MA): Addison-Wesley Publ.Company, 1979

48

Hyvöinen, A.; Karhunen, J.; Oja, E., Independent Component Analysis, John Wiley, 2001, (ISBN-10: 0471221317, ISBN-13: 978-0471221319)

49

Jordan, M.I. An Introduction to Linear algebra in Parallel Distributed Processing, In: Rumelhart, D.E.; McClelland, J.L.,(Eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol.1, Cambridge (MA) - London: The MIT Press, 1986, (ISBN: 0-262-18123-1), pp.365-422, 1986

50

Kappeler, P. Verhaltensbiologie, Berlin - Heidelberg - New York, Springer, 2006

51

Kandel, E.R. In Search of Memory. The emergence of a New Science of Mind, New York - London: W.W. orton & Company, 2006

52

Kandel, E.R.; Schwartz, J.H.; Jessell, T.M.; Siegelbaum, S.A.; Hudspeth, A.J.; (Eds.) Principles of Neural Science, 5th.ed., New York et.al: McGrawHill, 2012

61

Kasabov, N. Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach, In: IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, AUGUST 2008, 23-37

54

Kasabov N. Evolving Connectionist Systems. The Knowledge Engineering Approach. 2nd edition, Springer, New York. ISBN-10: 1-84628-345-0, 2007

55

Kasabov, N. Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines,Springer,London (2002)

56

Kasabov, N., ed. Future Directions for Intelligent Systems and Information Sciences, Heidelberg, Physica-Verlag (Springer Verlag) (2000), 420pp

57

Kasabov, N.; Kozma, R., editors Neuro-Fuzzy Techniques for Intelligent Information Systems, (Studies in Fuzziness and Soft Computing, Vol.30), Springer Verlag, 1999

58

Shun-Ichi Amari and Nikola K. Kasabov, editors Brain-Like Computing and Intelligent Information Systems, Springer Verlag, 1998

59

Kasabov, N., Kozma, R., Ko, K., O'Shea, R., Coghill, G., Gedeon, T., (Eds) Progress in Connectionist-based Information Systems, Vol. 1-2, pp.1355, Springer Verlag (1997) Volume 1: [ISBN 981-3083-61-1], Volume 2: [ISBN 981-3083-63-8]

60

Kasabov, N.K. Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, Massachussets, MIT Press (1996) 570p [ISBN 0 -262-11212-4]

61

Kasabov, N. Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach, In: IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, AUGUST 2008, 23-37

62

Kasabov, N., Data Mining and Knowledge Discovery Using Adaptive Neural Networks, Tutorial, IJCNN'03, Portland, USA

63

Kasabov, N., Goh, L., NeuCom - Environment for teaching and research in Bioinformatics, ISBM'2003, Brisbane, Australia

64

Kasabov, N., and Song, Q., DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its Application for Time Series Prediction, IEEE Transactions on Fuzzy Systems, vol. 10, no.2, April, (2002) 144-154.

65

Kasabov, N., Evolving Fuzzy Neural Networks for Supervised/Unsupervised On-Line, Knowledge-Based Learning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol 31, No. 6 Issue (December 2001, pp.902-918)

66

Kasabov, N. Artificial Neural Networks for Intelligent Information Processing, Transactions of Chemical Engineering, London, June 2001, 27:28

67

Kasabov, N. On-line learning, reasoning, rule extraction and aggregation in locally optimised evolving fuzzy neural networks, Neurocomputing, 41 (2001) 25-41

68

Kasabov, N. Evolving connectionist and fuzzy connectionist systems ? theory and applications for adaptive, on-line intelligent systems, In: Neuro-Fuzzy Techniques for Intelligent Information Processing, N. Kasabov and R. Kozma (eds.), Heidelberg Physica Verlag.

69

Kasabov, N. Evolving connectionist and fuzzy connectionist systems for on-line adaptive decision making and control, In: Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry (Eds.) Springer-Verlag.

70

Kasabov, N. Evolving connectionist systems for fast identification, classification and decision making, Australian Journal of Intelligent Information Processing Systems.

71

Kasabov, N. The ECOS framework and the 'eco' training method for evolving connectionist systems, Journal of Advanced Computational Intelligence, vol.2, No.6, 1-8.

72

Kernel-Machines List of Software: http://www.kernel-machines.org/

73

Kleene, S.C., Representation of events in nerve nets and finite automata, In: Shannon, C. E.; McCarthy,J.,(Eds.) Automata Studies, (AM-34) Annals of Mathematics Studies), Princeton (NJ): Princeton University Press, pp.3-41, 1956, (ISBN-10: 0691079161, ISBN-13: 978-0691079165)

74

Kleene, St.C. Introduction to Metamathematics, Groningen. Wolters-Noordhoff & Amsterdam: Noth-Holland Publishing company, 1952

75

Kleene, S.C., Representation of events in nerve nets and finite automata, RAND Corporation, 1951 /* An elementary exposition of the problems and results obtained during investigations in August, 1951, of the kinds of events any finite automation can respond to by assuming one of certain states */

76

Klir, G.J., Facets of Systems Science, New York - London: Plenum Press, 1991

77

Koch, C. The Quest for Consciousness. A Neurobiological Approach, Enlewood (Colorado): Roberts and Company Publishers, 2004

78

Kohonen, T., Self- Organization and Associative Memory, Berlin - Heidelberg - New York: Springer, 1984

79

Krumke, S.O.; Noltemeier, H. (2005), Graphentheoretische Konzepte und Algorithmen. Wiesbaden: B.G.Teubner Verlag

80

Ludwig, G. , Die Grundstrukturen einer physikalischen Theorie, Berlin - Heidelberg - New York: Springer, 1978

81

Mackintosh, N. J. (Ed.), Animal Learning and Cognition, Academic Press, 1994

82

Mallot, H.P.; Hübner, W.; Strzel, W., Neuronale Netze, In: (Eds.)Goerz, G.; Rollinger, C.-R.; Schneeberger, J.; (Eds.) Handbuch der Künstlichen Intelligenz, Mnchen-Wien: Oldenbourg Verlag, 4.Aufl., 2003, pp.73-124

83

, Marsden, J.E., Tromba, A.J. Vektoranalysis. Heidelberg - Berlin - Oxford: Spektrum Akademischer Verlag, 1995

84

MatLab,www.mathworks.com

85

McCulloch,W.S.; Pitts, W., A logical calculus of the ideas immanent in nervous activity, In: Bull of Math. Biophys., 5, 115-133, 1943 (reprint in: Shaw, G.L.; Palm, G. Brain Theory, Singapore: World Scientific, Pp.51-69)

86

McLeod, P.; Plunkett, K., ; Rolls, E.T. Introduction to Connectionist Modelling of Cognitive Processes, Oxford: Oxford University Press, 1998

87

Miikkulainen, R., Subsymbolic Natural Language Processing. An Integrated Model of Scripts, Lexicon, and Memory, Cambridge (MA): MIT Press, 1993

88

Minsky, M.L., Neural Nets and the Brain Model Problem, Ph.D. Dissertation, Princeton University, 1954.

89

Minsky, M. L., Computation: Finite and Infinite Machines, Englewood Cliffs (NJ): Prentice Hall, Inc.,1967

90

Minsky, M.L.; Papert, S., Perceptrons, Cambridge (MA): MIT Press, 1969 (2nd Ed. 1988)

91

Mittelstrass, J. (Ed.), Enzyklopädie Philosophie und Wissenschaftstheorie, Vol. 1-4, Stuttgart - Weimar: Publisher J.B.Metzler, 1995-1996

92

Nilsson, N. J., Artificial Intelligence: A New Synthesis, San Francisco (CA): Morgan Kaufmann Publ, 1998

93

O'Keefe, R.A.,,The Craft of Prolog, Cambridge (MA) - London: The MIT Press, 1990

94

OKSIMO,www.oksimo.org

95

OMG-SysML, OMG SysML Specification v. 1.0 (Final Adopted Specification) [May 2006]. http://www.omg.org/cgi-bin/doc?ptc/06-05-04 (Last visited March 21, 2007)

96

Perrin, D. Finite Automata, in: Van Leeuwen, J. (Ed.), Handbook of Theoretical Computer Science. Formal Models and Semantics. Vol.B, Amsterdam: Elsevier & Ca,bridge (MA): The MIT Press, 1990; Pp.1-57

97

Plunkett, K.; Elman, J.L., Exercises in Rethinking Innateness. A Handbook for Connectionist Simulations, Cambridge (MA): MIT Press, 1997

98

Rich, E. Artificial Intelligence, New York: McGraw-Hill,1983

99

Ritter, H.; Martinetz, Th.; Schulten, K. Neuronale Netze. Eine Einführung in die Neuroinformatik selbstorganisierender Netze, Bonn - München et al: Addison-Wesley Publishing Company, 1991, 2nd.rev.ed.

100

Roth, G., Das Gehirn und seine Wirklichkeit, 8.Aufl., Suhrkamp-Verlag, 2000, (ISBN-10: 351828875X, ISBN-13: 978-3518288757)

101

Rumelhart, D.E.; McClelland, J.L.,(Eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol.1+2, Cambridge (MA) - London: The MIT Press, 1986, (ISBN: 0-262-18123-1).

102

Rojas, R., Theorie der neuronalen Netze. Eine systematische Einfhrung, Berlin: Springer, 1996, (ISBN: 3540563539).

103

Rosenblatt, F., The perceptron. A probabilistic model for information storage and organization in the brain, In: Psychological Reviews, 65 (1958): S. 386-408.

104

Rosenblatt, F., Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Washinton d.C.: Spartan Books, 1962

105

, Salas, S.L.; Hill, E. Calculus. Einführung in die Differential- und Integralrechnung. Heidelberg - Berlin - Oxford: Spektrum akademischer Verlag, 1990.

106

J. Schmidhuber Goedel machines: Fully Self-Referential Optimal Universal Self-Improvers, In B. Goertzel and C. Pennachin, eds.: Artificial General Intelligence, p. 199-226, 2006

107

J. Schmidhuber, S. Hochreiter, Y. Bengio Evaluating benchmark problems by random guessing, In S. C. Kremer and J. F. Kolen, eds., A Field Guide to Dynamical Recurrent Neural Networks. IEEE press, 2001

108

. J. Schmidhuber, J. Zhao, N. Schraudolph Reinforcement learning with self-modifying policies, In S. Thrun and L. Pratt, eds., Learning to learn, Kluwer, pages 293-309, 1997.

109

J. Schmidhuber. A general method for incremental self-improvement and multiagent learning, In X. Yao, editor, Evolutionary Computation: Theory and Applications. Chapter 3, pp.81-123, Scientific Publ. Co., Singapore, 1999 (submitted 1996).

110

. J. Schmidhuber Learning to control fast-weight memories: An alternative to recurrent nets, Neural Computation, 4(1):131-139, 1992

111

J. Schmidhuber Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem (Dynamic neural nets and the fundamental spatio-temporal credit assignment problem), Dissertation, Institut für Informatik, Technische Universität München, 1990

112

J. Schmidhuber A local learning algorithm for dynamic feedforward and recurrent networks, Connection Science, 1(4):403-412, 1989. (The Neural Bucket Brigade - figures omitted!)

113

Schmidt, R.F.; Lang, F.; Thews, G., Physiologie des Menschen mit Pathophysiologie, 28. korr. u. aktualis. Aufl., Berlin: Springer, 2005,( ISBN-10: 3540667334, ISBN-13: 978-3540667339)

114

Schumpeter, J.A. Geschichte der ökonomischen Analyse, Bd.1+2, Götingen (Germany): Vandenhoek & Ruprecht GmbH & Co. KG, 2007. (Translated from the englisch original History of Economic Analysis, ed. from Manuscript by Elisabeth Boody Schumpeter, London: George Allen & Unwin Ltd., 1965).

115

SciLab,www.scilab.org

116

Claude E. Shannon A mathematical theory of communication. Bell System Tech. J., 27:379-423, 623-656, July, Oct. 1948 (see URL: http://cm.bell-labs.com/cm/ms/what/shannonday/paper.html; last visited May-15, 2008)

117

Shannon, C.E.; Weaver, W. The Mathematical Theory of Communication, Urbana ( Illinois): The University of Illinois Press,1949, ISBN 0-252-72548-4 (German: Mathematische Grundlagen der Informationstheorie, München - Wien: R.Oldenbourg Verlag, 1976)

118

Shaw, G.L.; Palm, G., (Eds.),Brain Theory: Reprint Volume: v. 1. (World Scientific Advance Series on Neuroscience Vol.1), 1988, Singapore - New Jersey - Hongkong: World Scientific, ISBN-10: 9971504847, ISBN-13: 978-9971504847

119

Shepherd, G.M., Neurobiology, 3rd.Ed, New York - Oxford: Oxford University Press, 1994, (ISBN-10: 0195088433, ISBN-13: 978-0195088434)

120

Slepian, D. (editor) Key Papers in the Development of Information Theory, New York: IEEE Press, 1974

121

Sloane, N. J. A.; Wyner, A. D. (editors) Claude Elwood Shannon: Collected Papers, New York: IEEE Press, 1993.

122

Sneed, J. D., The Logical Structure of Mathematical Physics, (2nd.rev.ed.) Dordrecht - Boston - London: D.Reidel Publishin Company, 1979

123

Stingl, P. Mathematik für Fachhochschulen. Technik und Informatik. München - Wien. Calr Hanser Verlag, 1999 (6th.rev.Ed.)

124

Storch, V.; Welsch, U.; Wink, M. (Eds.)Evolutionsbiologie, Berlin - Heidelberg: Springer, 2nd. rev.ed., 2007

125

Suppe, F. (Ed.), The Structure of Scientific Theories, 2nd. ed., Urbana: University of Illinois Press, 1979

126

Thompson, R.F. , Das Gehirn. Von der Nervenzelle bis zur Verhaltenssteuerung, (3nd. ed.), Heidelberg - Berlin: Spektrum Akademischer Verlag, 2001

127

Turing, A. M. On Computable Numbers with an Application to the Entscheidungsproblem. In: Proc. London Math. Soc., Ser.2, vol.42(1936), pp.230-265; received May 25, 1936; Appendix added August 28; read November 12, 1936; corr. Ibid. vol.43(1937), pp.544-546. Turing's paper appeared in Part 2 of vol.42 which was issued in December 1936 (Reprint in M.DAVIS 1965, pp.116-151; corr. ibid. pp.151-154).

128

Wagner, F.; Schmuki, R.; Wagner, Th.; Wolstenholme, P. Modeling Software with Finite Machines. A Practical Approach, Boca Raton - New York: Auerbach Publications, 2006

129

Watts, M.J. A Decade of Kasabov's Evolving Connectionist Systems: A Review, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, VOL. 39, NO. 3, MAY 2009, pp.253-269

130

Watts, M.J. Nominal-scale Evolving Connectionist Systems, Neural Networks, 2006. IJCNN '06. International Joint Conference on, pp. 2055 - 2059

131

Watts, M.J. Evolving Connectionist Systems. Characterization, Simplification, Formalization, Explanation and Optimization, PhD-Thesis, University of Otago, Dunedin, New Zealand, 2004

132

WHITROW, G.J. The Natural Philosophy of Time, Oxford: Clarendon Press, 1980 2nd ed., repr. 1990

133

Zell, A., Simulation Neuronaler Netze, Oldenbourg, 1994, ( ISBN-10: 3486243500, ISBN-13: 978-3486243505)