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- W4243595401 abstract "This chapter proposes a novel approach to fuzzy modeling of human working memory (WM) using electroencephalography (EEG) signals, acquired during human face encoding and recall experiments in connection with a face recognition problem. The novelty of the proposed method lies in modeling of the human WM from the acquired EEG features of its input and output using the concept of fuzzy associative memory (FAM). The chapter provides the principles and methodologies used to formulate and compute inverse fuzzy relation. It explains the experiments undertaken for the fuzzy modeling of human WM and results obtained from the experiments. A comparative study of the performance of the proposed FAM model realized with the inverse formulations by the existing techniques is also undertaken. The proposed WM model supports inter-person variability, i.e. the weight matrices obtained for two different persons by the same subject are sufficiently different." @default.
- W4243595401 created "2022-05-12" @default.
- W4243595401 date "2020-08-28" @default.
- W4243595401 modified "2023-10-18" @default.
- W4243595401 title "Working Memory Modeling Using Inverse Fuzzy Relational Approach" @default.
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- W4243595401 doi "https://doi.org/10.1002/9781119705925.ch2" @default.
- W4243595401 hasPublicationYear "2020" @default.
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