Abstract
Pattern recognition is important in virtually all intelligent systems, in particular, in human centered systems, one major development is the intelligent servant modules (ISMs) that can react and interact with humans. To effectively respond to human’s request ISMs must be able to recognize gestures, voice patterns, facial features and so on. Making systems to achieve such capabilities is a challenging task due to uncertainties which arise from incomplete or imprecise knowledge of what is being perceived together with data corruption due to inherent noise in sensors. Furthermore, the recognition system must be able to generalize from the “seen” samples to “unseen” patterns that are from the same population. In addition, the system will have to reject “unknown” patterns or to update the knowledge base, in some instances, to alert the user.
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Liu, ZQ., Chandrasekaran, V. (2000). Fuzzy Gated Neural Networks in Pattern Recognition. In: Liu, ZQ., Miyamoto, S. (eds) Soft Computing and Human-Centered Machines. Computer Science Workbench. Springer, Tokyo. https://doi.org/10.1007/978-4-431-67907-3_8
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DOI: https://doi.org/10.1007/978-4-431-67907-3_8
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