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Chaotic Neural Networks in Quantum Finance

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Abstract

This chapter examines an interesting topic—chaotic neural oscillators in quantum finance. First, it explores quantum world of chaotic oscillation from the multiverse in quantum theory, chaos in brain to our sensory of oscillations. Next, it reviews chaotic neural oscillators, neuroscience motivation, Wang-oscillators, and the author’s latest research on Lee-oscillators; its neural dynamics and applications. After that, it studies quantum finance oscillators (QFO) using Lee-oscillators and different applications of QFO in quantum finance including quantum financial prediction using chaotic neural networks, chaotic deep neural networks and chaotic intelligent multiagent-based trading systems. As it reveals, chaotic neural oscillators can be adopted into almost any AI tools and technologies to improve efficiency and performance. Also, all these various kinds of AI tools and technologies are just different tools and methods to model and handle real-world problems, finance in our case, which is highly complex and chaotic in nature.

If we look at the way the universe behaves, quantum mechanics gives us fundamental, unavoidable indeterminacy, so that alternative histories of the universe can be assigned probability.

Murray Gell-Mann (1929–2019)

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References

  • Chen, L. and Aihara, K. (1995) Chaotic stimulated annealing by a neural model with transient chaos. Neural Networks. 8(6): 915–930.

    Article  Google Scholar 

  • Falcke, M. et al. (2000) Modeling observed chaotic oscillators in bursting neurons: The role of calcium dynamics and IP3. Biol. Cybern. 82: 517–527.

    Article  Google Scholar 

  • Fernandes, T. and Malsburg, C. (2015) Self-organization of control circuits for invariant fiber projections. Neural Computation. 27(5): 1005–1032.

    Article  MathSciNet  Google Scholar 

  • Freeman, W. J. (2013) Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. Springer.

    Google Scholar 

  • Freeman, W. J. (2008) A pseudo-equilibrium thermodynamic model of information processing in nonlinear brain dynamics. Neural Network. 21(2–3): 257–265.

    Article  Google Scholar 

  • Freeman, W. J. (2000) Neurodynamics: An Exploration in Mesoscopic Brain Dynamics (Perspectives in Neural Computing). Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hoshino, O. et al. (2003) Roles of dynamic linkage of stable attractors across cortical networks in recalling long-term memory. Biol. Cybern., 88(3): 163–176.

    Article  Google Scholar 

  • Lee, R. S. T. (2006) LEE-Associator – A Transient Chaotic Autoassociative Network for Progressive Memory Recalling. Neural Networks. 19(5): 644–666.

    Article  Google Scholar 

  • Lee, R. S. T. (2004) A Transient-chaotic Auto-associative Network (TCAN) based on LEE-oscillators. IEEE Trans. Neural Networks. 15(5): 1228–1243.

    Article  Google Scholar 

  • Malsburg, C. (1995) Binding in models of perception and brain function. Current Opinion in Neurobiology. 5(4): 520–526.

    Article  Google Scholar 

  • Tegmark, M. (2014) Our Mathematical Universe: My Quest for the Ultimate Nature of Reality. Vintage.

    Google Scholar 

  • Tuchong (2019a) What would happen if time stopped? http://stock.tuchong.com/image?imageId=446677423538045208. Accessed 21 Aug 2019.

  • Tuchong (2019b) Artificial Neural Networks. http://stock.tuchong.com/image?imageId=492056055110172742. Accessed 21 Aug 2019.

  • Wang, X. (1991) Period-doublings to chaos in a simple neural network: An analytic proof. Complex Syst. 5: 425–441.

    Google Scholar 

  • Zhou, C. and Chen, T. (2000) Chaotic neural networks and chaotic annealing. Neurocomputing. 30(1): 293–300.

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College (UIC), Zhuhai, Guangdong, China

    Raymond S. T. Lee

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  1. Raymond S. T. Lee
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Correspondence to Raymond S. T. Lee .

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Lee, R.S.T. (2020). Chaotic Neural Networks in Quantum Finance. In: Quantum Finance. Springer, Singapore. https://doi.org/10.1007/978-981-32-9796-8_9

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  • DOI: https://doi.org/10.1007/978-981-32-9796-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9795-1

  • Online ISBN: 978-981-32-9796-8

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