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Dynamic evolving fuzzy neural networks with `m-out-of-n' activation nodes for on-line adaptive systems
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, ...
Adaptive, evolving, hybrid connectionist systems for image pattern recognition
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 ...
Spatial-temporal adaptation in evolving fuzzy neural networks for on-line adaptive phoneme recognition
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 ...
Evolving connectionist systems for on-line, knowledge-based learning: Principles and applications
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. ...
From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition
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 ...
The concepts of hidden Markov model in speech recognition
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 ...
Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems
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 ...