The Computational Theory of Intelligence: Feedback - International Journal of Modern Nonlinear Theory and Application

IJMNTA > Vol.6 No.2, June 2017

International Journal of Modern Nonlinear Theory and Application

Volume 6, Issue 2 (June 2017)

ISSN Print: 2167-9479 ISSN Online: 2167-9487

Google-based Impact Factor: 0.27 Citations

The Computational Theory of Intelligence: Feedback ()

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DOI: 10.4236/ijmnta.2017.62006 1,409 Downloads 2,348 Views Citations

Author(s)

Daniel Kovach^*

Affiliation(s)

Quantitative Research, Institute of Trading and Finance, Montreal, Canada.

ABSTRACT

In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in order that the entropy minimization principal of computational intelligence is preserved in the presence of feedback.

KEYWORDS

Neural Networks, Feedback, Intelligence, Computation, Artificial Intelligence, Lyapunov Stability

Share and Cite:

Kovach, D. (2017) The Computational Theory of Intelligence: Feedback. International Journal of Modern Nonlinear Theory and Application, 6, 70-73. doi: 10.4236/ijmnta.2017.62006.

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