Abstract
This chapter introduces three major artificial intelligence (AI) tools in quantum finance: artificial neural networks (ANNs) on machine learning and time series prediction, fuzzy logics (FLs) on fuzzy and inexact financial modeling and genetic algorithms (GAs) on trading strategy optimization. More importantly, it shows how these AI tools are integrated with quantum finance technology to implement quantum finance forecast and intelligent agent-based program trading systems. First, it starts with a general overview of AI technology. Second, it explores the brain of AI—artificial neural networks (ANNs), which includes biological neural networks, neuron model, classification of ANNs, typical ANNs such as Hopfield network and feedforward backpropagation Network. Third, it explores the optimization engine of AI—genetic algorithms (GAs), which includes general overview of GA, system flow of GA and key operations in GA implementation. After that, it explores the fuzzification engine of AI—fuzzy logics, which includes basic concepts of FL, relationship between FL and uncertainty principle, ICMR fuzzy logic model, applications of fuzzy logic in daily life, fuzzy expert, and fuzzy-neuro systems in quantum finance.
A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.
Alan Turing (1912–1954)
© Portions of this chapter are reprinted from Lee (2006), with permission of Springer Nature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abraham, T. Rebel (2016) Genius: Warren S. McCulloch’s Transdisciplinary Life in Science. MIT Press, Cambridge.
Black, M. (1937) Vagueness: An Exercise in Logical Analysis. Philosophy of Science 4(4): 427–455.
Bentivoglio, M. (2014) “Golgi, Camillo”. In Daroff, Robert B.; Aminoff, Michael J. Encyclopedia of the Neurological Sciences (Second edition. ed.). Burlington: Elsevier Science. 464–466.
Darwin, D. and Huxley, J. (2003) The Origin of Species: 150th Anniversary Edition. Signet; Reprint, Anniversary edition.
Fausett, L. (1994) Fundamentals of Neural Networks Architectures Algorithms and Applications. Prentice Hall.
Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional.
Hansen, W. Classical Mythology: A Guide to the Mythical World of the Greeks and Romans. Oxford University Press, 2005.
Hebb, D.O. (1949) The Organization of Behavior. New York: Wiley & Sons.
Holland, J. H. (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. A Bradford Book; Reprint edition.
Hopfield, J.J. (1984) Neurons with Graded Response Have Collective Computational Properties like Those of Two-State Neurons. Proceedings of the National Academy of Sciences of the United States of America. 81(10): 3088–3092.
Kramer, O. (2017) Genetic Algorithm Essentials (Studies in Computational Intelligence). Springer.
Lee, R. S. T. Fuzzy-Neuro Approach to Agent Applications (From the AI Perspective to Modern Ontology). Springer-Verlag, Heidelberg Germany, 2006.
Lee, R. S. T. A Transient-chaotic Auto-associative Network (TCAN) based on LEE-oscillators. IEEE Transactions on Neural Networks. 15(5): 1228–1243, 2004.
McCulloch, W. and Walter, P. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics. 5 (4): 115–133, 1943.
Patterson, D. W. (1996) Artificial Neural Networks. Prentice Hall.
Ross, T. J. (2016) Fuzzy Logic with Engineering Applications. Wiley.
Russell, S. and Norvig, P. (2015) Artificial Intelligence: A Modern Approach, 3rd edition. Pearson Education India.
Siler, W. and Buckley, J. J. (2004) Fuzzy Expert Systems and Fuzzy Reasoning. Wiley-Interscience.
Silva, et al. (2017) Artificial Neural Networks: A Practical Course. Springer.
Tuchong (2019a) Illustration of AI. http://stock.tuchong.com/image?imageId=286594758192922638. Accessed 21 Aug 2019.
Tuchong (2019b) Weak AI. http://stock.tuchong.com/image?imageId=467459610627670027. Accessed 21 Aug 2019.
Tuchong (2019c) 3D illustration of integrate-and-fire in neural network. http://stock.tuchong.com/image?imageId=483388184041619483. Accessed 21 Aug 2019.
Tuchong (2019d) Neural network and AI. https://stock.tuchong.com/image?imageId=490703320800559171. Accessed 21 Aug 2019.
Tuchong (2019e) Populations of chromosomes. http://stock.tuchong.com/image?imageId=453246275369566360. Accessed 21 Aug 2019.
Tuchong (2019f) Smart Home with Fuzzy Logic Electronic Home Appliances. http://stock.tuchong.com/image?imageId=267053275471413372. Accessed 21 Aug 2019.
Turing, A. (1950) Computing Machinery and Intelligence. Mind, LIX (236): 433–460.
Zadeh, L. A. and Aliev, R. A. (2019) Fuzzy Logic Theory and Applications: Part I and Part II. World Scientific Publishing Company.
Zadeh, L. A. (1975a) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-I. Information Sciences. 8(1): 199–249.
Zadeh, L. A. (1975b) The Concept of a Linguistic Variable and its Application to Approximate Reasoning-II. Information Sciences. 8(4): 301–357.
Zadeh, L. A. (1975c) The Concept of a Linguistic Variable and its Application to Approximate Reasoning-III. Information Sciences. 9(1): 43–80.
Zadeh, L. A. (1965) Fuzzy sets. Information and Control. 8 (3): 338–353.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Lee, R.S.T. (2020). AI Powerful Tools in Quantum Finance. In: Quantum Finance. Springer, Singapore. https://doi.org/10.1007/978-981-32-9796-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-32-9796-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9795-1
Online ISBN: 978-981-32-9796-8
eBook Packages: Computer ScienceComputer Science (R0)