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    Dynamic evolving fuzzy neural networks with `m-out-of-n' activation nodes for on-line adaptive systems 

    Kasabov, Nikola; Song, Qun
    The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, ...
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    Adaptive, evolving, hybrid connectionist systems for image pattern recognition 

    Kasabov, Nikola; Israel, Steven; Woodford, Brendon J
    The chapter presents a new methodology for building adaptive, incremental learning systems for image pattern classification. The systems are based on dynamically evolving fuzzy neural networks that are neural architectures ...
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    Spatial-temporal adaptation in evolving fuzzy neural networks for on-line adaptive phoneme recognition 

    Kasabov, Nikola; Watts, Michael
    The paper is a study on a new class of spatial-temporal evolving fuzzy neural network systems (EFuNNs) for on-line adaptive learning, and their applications for adaptive phoneme recognition. The systems evolve through ...
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    Evolving connectionist systems for on-line, knowledge-based learning: Principles and applications 

    Kasabov, Nikola
    The paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. ...
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    From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition 

    Kasabov, Nikola; Kilgour, Richard; Sinclair, Stephen
    This paper discusses the problem of adaptation in automatic speech recognition systems (ASRS) and suggests several strategies for adaptation in a modular architecture for speech recognition. The architecture allows for ...
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    The concepts of hidden Markov model in speech recognition 

    Abdulla, Waleed H; Kasabov, Nikola
    The speech recognition field is one of the most challenging fields that has faced scientists for a long time. The complete solution is still far from reach. The efforts are concentrated with huge funds from the companies ...
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    Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems 

    Kim, Jaesoo; Kasabov, Nikola
    In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides ...

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    Kasabov, Nikola (7)
    Abdulla, Waleed H (1)Israel, Steven (1)Kilgour, Richard (1)Kim, Jaesoo (1)... View MorePublication Date
    1999 (7)
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    Discussion Paper (7)
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