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oaioai:scholarsmine.mst.edu:doctoral_dissertations-2884

The development of hybrid intelligent systems for technical analysis based equivolume charting

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Publication date
1 January 2007
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Scholars\u27 Mine

Abstract

This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks --Introduction, page 1

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Missouri University of Science and Technology (Missouri S&T): Scholars' Mine

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Last time updated on 17/10/2019

This paper was published in Missouri University of Science and Technology (Missouri S&T): Scholars' Mine.

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