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2016 | OriginalPaper | Buchkapitel

Artificial Neural Network Modelling: An Introduction

verfasst von : Subana Shanmuganathan

Erschienen in: Artificial Neural Network Modelling

Verlag: Springer International Publishing

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Abstract

While scientists from different disciplines, such as neuroscience, medicine and high performance computing, eagerly attempt to understand how the human brain functioning happens, Knowledge Engineers in computing have been successful in making use of the brain models thus far discovered to introduce heuristics into computational algorithmic modelling. Gaining further understanding on human brain/nerve cell anatomy, structure, and how the human brain functions, is described to be significant especially, to devise treatments for presently described as incurable brain and nervous system related diseases, such as Alzheimer’s and epilepsy. Despite some major breakthroughs seen over the last few decades neuroanatomists and neurobiologists of the medical world are yet to understand how we humans think, learn and remember, and how our cognition and behaviour are linked. In this context, the chapter outlines the most recent human brain research initiatives following which early Artificial Neural Network (ANN) architectures, components, related terms and hybrids are elaborated.

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Literatur
4.
Zurück zum Zitat S.-i. Amari, Foreword, in Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering, ed. by N.K. Kasabov (USA, Bradford, 1995), p. xi S.-i. Amari, Foreword, in Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering, ed. by N.K. Kasabov (USA, Bradford, 1995), p. xi
5.
Zurück zum Zitat J. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan, 1975 J. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan, 1975
6.
Zurück zum Zitat G. Matsumato, The Brain can Acquire its Algorithm in a Self-Organized Fashion, in Proceedings of the ICONIP/ANZIIS/ANNES’99 International Workshop, Perth, Australia, 22–23 Nov 1999, p. 116 G. Matsumato, The Brain can Acquire its Algorithm in a Self-Organized Fashion, in Proceedings of the ICONIP/ANZIIS/ANNES’99 International Workshop, Perth, Australia, 22–23 Nov 1999, p. 116
7.
Zurück zum Zitat T.M. Mitchell, Machine learning and Data Mining. Communications of the ACM, Nov 1999, vol 42, No. 11 T.M. Mitchell, Machine learning and Data Mining. Communications of the ACM, Nov 1999, vol 42, No. 11
8.
Zurück zum Zitat L. Fu, Knowledge discovery basedon neural networks. Communications of the ACM, Nov 1999, vol. 42. No. 11, pp. 47–50 L. Fu, Knowledge discovery basedon neural networks. Communications of the ACM, Nov 1999, vol. 42. No. 11, pp. 47–50
10.
Zurück zum Zitat N.K. Kasabov, Foundations of Neural Networks, A Bradford Book, (The MIT Press, Fuzzy systems and Knowledge Engineering, Cambridge, Massachusetts, London, England, 1995), 581 pp N.K. Kasabov, Foundations of Neural Networks, A Bradford Book, (The MIT Press, Fuzzy systems and Knowledge Engineering, Cambridge, Massachusetts, London, England, 1995), 581 pp
11.
Zurück zum Zitat T. Kohonene, G. Deboeck, Visual Explorations in Finance with Self-organizing Maps, (Springer, London, 1998), 258 pp T. Kohonene, G. Deboeck, Visual Explorations in Finance with Self-organizing Maps, (Springer, London, 1998), 258 pp
12.
Zurück zum Zitat W. Pitts, W.S. McCulloch, How we know univesrsals, The perception of auditory and visual forms. Bull. Math. Biophys. 9(3), 127–147 (1947)CrossRef W. Pitts, W.S. McCulloch, How we know univesrsals, The perception of auditory and visual forms. Bull. Math. Biophys. 9(3), 127–147 (1947)CrossRef
13.
Zurück zum Zitat W.S. McCulloch, W.H. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943) W.S. McCulloch, W.H. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)
14.
Zurück zum Zitat B. Widrow, Generalization and information storage in networks of Adaline ‘Neurons’, in Self-Organizing Systems, symposium proceedings, ed. by M.C. Yovitz, G.T. Jacobi, G. Goldstein (Spartan Books, Washington, DC, 1962), pp. 435–461 B. Widrow, Generalization and information storage in networks of Adaline ‘Neurons’, in Self-Organizing Systems, symposium proceedings, ed. by M.C. Yovitz, G.T. Jacobi, G. Goldstein (Spartan Books, Washington, DC, 1962), pp. 435–461
15.
Zurück zum Zitat B. Widrow, Review of “Adaptive Systems of Logic Network and Binary Memories”, Trans. Electron. Comput.: IEEE J. Aleksander EC16(5), 710–711, (1967) B. Widrow, Review of “Adaptive Systems of Logic Network and Binary Memories”, Trans. Electron. Comput.: IEEE J. Aleksander EC16(5), 710–711, (1967)
16.
Zurück zum Zitat B. Widrow, J.B. Angell, Reliable, trainable networks for computing and control. Aerosp. Eng. 21(9), 78–123 (1962) B. Widrow, J.B. Angell, Reliable, trainable networks for computing and control. Aerosp. Eng. 21(9), 78–123 (1962)
17.
Zurück zum Zitat B. Widrow, M.E. Hoff, Associative Storage and Retrieval of Digital Information in Networks of Adaptive `Neurons’. Biol. Prototypes Synth. Syst. 1, 160 (1962)CrossRef B. Widrow, M.E. Hoff, Associative Storage and Retrieval of Digital Information in Networks of Adaptive `Neurons’. Biol. Prototypes Synth. Syst. 1, 160 (1962)CrossRef
18.
Zurück zum Zitat M.L. Minsky, S. Papert, Perceptrons: An Introduction to Computational Geometry (MIT Press, Cambridge, MA, 1969)MATH M.L. Minsky, S. Papert, Perceptrons: An Introduction to Computational Geometry (MIT Press, Cambridge, MA, 1969)MATH
20.
Zurück zum Zitat J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558 (1982)MathSciNetCrossRef J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558 (1982)MathSciNetCrossRef
21.
Zurück zum Zitat D.E. Rumelhart, G. Hinton, R. Williams, Learning internal representation by error propagation, in D.E. Rumelhart, J.L. McClelland, PDP Research Group (eds), Parallel Distributed Processing Exploration in the Microstructure of Cognition: Foundations, vol. 1 (MIT Press, Cambridge, MA, 1986) p. 1 D.E. Rumelhart, G. Hinton, R. Williams, Learning internal representation by error propagation, in D.E. Rumelhart, J.L. McClelland, PDP Research Group (eds), Parallel Distributed Processing Exploration in the Microstructure of Cognition: Foundations, vol. 1 (MIT Press, Cambridge, MA, 1986) p. 1
22.
Zurück zum Zitat P. Werbos, Backpropagation through time: What it does and how to do it., Proc. IEEE 87, 10 (1990) P. Werbos, Backpropagation through time: What it does and how to do it., Proc. IEEE 87, 10 (1990)
23.
Zurück zum Zitat G.A. Carpenter, S. Grossberg, ART 2: Stable self-organization of pattern recognition codes for analog input patterns. Appl. Opt. 26, 4919–4930 (1987)CrossRef G.A. Carpenter, S. Grossberg, ART 2: Stable self-organization of pattern recognition codes for analog input patterns. Appl. Opt. 26, 4919–4930 (1987)CrossRef
24.
Zurück zum Zitat G.A. Carpenter, S. Grossberg, A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vision, Graph., Image Proc. 37, 54–115 (1987)CrossRefMATH G.A. Carpenter, S. Grossberg, A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vision, Graph., Image Proc. 37, 54–115 (1987)CrossRefMATH
27.
Zurück zum Zitat T. Moody, C. Darken, Fast learning in networks of locally tuned processing units. Neural Comput. 1, 281–294 (1989)CrossRef T. Moody, C. Darken, Fast learning in networks of locally tuned processing units. Neural Comput. 1, 281–294 (1989)CrossRef
28.
Zurück zum Zitat J. Taylor, C. Mannion, New Developments in Neural Computing (Adam Hilger, Bristol, England 1989) J. Taylor, C. Mannion, New Developments in Neural Computing (Adam Hilger, Bristol, England 1989)
29.
Zurück zum Zitat I. Aleksander, Neural Computing Architectures. The Design of Brain-like Machines, in I. Aleksander, H. Morton (eds.), (MIT Press, Cambridge, MA, 1990) (An Introduction to Neural Computing, London, Chapman & Hall., 1989) I. Aleksander, Neural Computing Architectures. The Design of Brain-like Machines, in I. Aleksander, H. Morton (eds.), (MIT Press, Cambridge, MA, 1990) (An Introduction to Neural Computing, London, Chapman & Hall., 1989)
30.
Zurück zum Zitat T. Yamakawa, Pattern recognition hardware system employing a fuzzy neuron, in Proceedings of the International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, July 1990, pp. 943–948 T. Yamakawa, Pattern recognition hardware system employing a fuzzy neuron, in Proceedings of the International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, July 1990, pp. 943–948
31.
Zurück zum Zitat T. Furuhashi, T. Hasegawa, S. Horikawa et al., An adaptive fuzzy controller using fuzzy neural, in networks. In: Proceedings of Fifth International Fuzzy Systems Association World Congress, IEEE (1993), pp. 769–772 T. Furuhashi, T. Hasegawa, S. Horikawa et al., An adaptive fuzzy controller using fuzzy neural, in networks. In: Proceedings of Fifth International Fuzzy Systems Association World Congress, IEEE (1993), pp. 769–772
32.
Zurück zum Zitat W. Freeman, C. Skarda, Spatial EEG patterns, non-linear dynamics and perception: The neo-Sherringtonian view. Brain Res. Rev. 10(10), 147–175 (1985)CrossRef W. Freeman, C. Skarda, Spatial EEG patterns, non-linear dynamics and perception: The neo-Sherringtonian view. Brain Res. Rev. 10(10), 147–175 (1985)CrossRef
33.
Zurück zum Zitat K. Kaneko, Clustering, coding, switching, hierarchical ordering, and control in network of chaotic elements. Physica 41D, 137–172 (1990)MathSciNetMATH K. Kaneko, Clustering, coding, switching, hierarchical ordering, and control in network of chaotic elements. Physica 41D, 137–172 (1990)MathSciNetMATH
34.
Zurück zum Zitat R. Borisyuk, A. Kirillov, Bifurcation analysis of neural network model. Biol. Cybern. 66, 319–325 (1992)CrossRefMATH R. Borisyuk, A. Kirillov, Bifurcation analysis of neural network model. Biol. Cybern. 66, 319–325 (1992)CrossRefMATH
35.
Zurück zum Zitat B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Approach to Machine Intelligence (Englewood Cliffs, NJ, Prentice-Hall, 1992)MATH B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Approach to Machine Intelligence (Englewood Cliffs, NJ, Prentice-Hall, 1992)MATH
36.
Zurück zum Zitat F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the Brain. Psychol. Rev. 65, 386–408 (1958)MathSciNetCrossRef F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the Brain. Psychol. Rev. 65, 386–408 (1958)MathSciNetCrossRef
37.
Zurück zum Zitat B. Widrow, M.E. Hoff, Adaptive switching circuits. IRE WESCON Convention Rec. N.Y. 4, 96–104 (1960) B. Widrow, M.E. Hoff, Adaptive switching circuits. IRE WESCON Convention Rec. N.Y. 4, 96–104 (1960)
38.
Zurück zum Zitat A. Newell, H.A. Simon, Human Problem Solving, Englewood Cliffs (Prentice Hall, NJ, 1972) A. Newell, H.A. Simon, Human Problem Solving, Englewood Cliffs (Prentice Hall, NJ, 1972)
39.
Zurück zum Zitat P. Smolenski, Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artif. Intell. 46, 159–216 (1990)MathSciNetCrossRef P. Smolenski, Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artif. Intell. 46, 159–216 (1990)MathSciNetCrossRef
Metadaten
Titel
Artificial Neural Network Modelling: An Introduction
verfasst von
Subana Shanmuganathan
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-28495-8_1

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