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• W1843304674 abstract "This paper describes a framework that utilizes an adaptive-network-based fuzzy inference system to perform user-constrained pattern recognition on time-series data. Using a customizable fuzzy logic grammar, the architecture allows an analyst to capture domain expertise in a context-relevant manner. Fuzzy logic rules constructed by the analyst are used to perform feature extraction and influence the training of a neural network to perform pattern recognition. We demonstrate that the architecture is capable of performing noise-tolerant searches across multiple features on large volumes of time-series data. The experiments presented here are from the domain of stock analysis. We are able to create simple rule sets automatically to search a data warehouse of stocks to select stocks that exhibit desirable behaviours. Copyright © 2011 John Wiley & Sons, Ltd." @default.
• W1843304674 created "2016-06-24" @default.
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• W1843304674 date "2011-01-01" @default.
• W1843304674 modified "2023-10-18" @default.
• W1843304674 title "Neuro-fuzzy time-series analysis of large-volume data" @default.
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• W1843304674 doi "https://doi.org/10.1002/isaf.322" @default.
• W1843304674 hasPublicationYear "2011" @default.
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