Results 201 to 210 of about 53,858 (257)
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A neuro-fuzzy system for inferencing
International Journal of Intelligent Systems, 1999We justify the need for a connectionist implementation of compositional rule of inference (COI) and propose a network architecture for the same. We call it COIN-the compositional rule of inferencing. Given a relational representation of a set of rules, the proposed architecture can realize the COI.
Kuhu Pal, Nikhil R. Pal
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Neuro-Fuzzy Relational Classifiers
2004In the paper, we present a new fuzzy relational system with multiple outputs for classification purposes. Rules in the system are more flexible than the rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights comes from an additional binary relation. Thanks to this, input and output fuzzy sets are related to
Rafal Scherer, Leszek Rutkowski
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Third International Conference on Natural Computation (ICNC 2007), 2007
The research in the paper is about the hardware machine learning study of an intelligent neuro-fuzzy system (NFS). The NFS is embedded within a DSP-FPGA chip system. The well-known random optimization method is used as the learning algorithm for the NFS.
Chunshien Li, Zhao-Yi Tsai
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The research in the paper is about the hardware machine learning study of an intelligent neuro-fuzzy system (NFS). The NFS is embedded within a DSP-FPGA chip system. The well-known random optimization method is used as the learning algorithm for the NFS.
Chunshien Li, Zhao-Yi Tsai
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Neuro-fuzzy Model-based Control
Journal of Intelligent and Robotic Systems, 1998The paper deals with the Neuro-fuzzy model-based control and its application. Various types of the fuzzy logic and neural-net-based nonlinear autoregressive models with exogenous variables are reviewed with respect to the model error. Two types of model-based neuro-fuzzy control – a cancellation controller and a predictive controller are reviewed – and
Drago Matko +2 more
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2002
In video sequence processing, shadow remains a major source of error of object segmentation. Traditional methods of shadow removal are mainly based on colour difference thresholding between the background and current images. The application of colour filters on MPEG or MJPEG images, however, is often erroneous as the chrominace information is ...
Benny P. L. Lo, Guang-Zhong Yang
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In video sequence processing, shadow remains a major source of error of object segmentation. Traditional methods of shadow removal are mainly based on colour difference thresholding between the background and current images. The application of colour filters on MPEG or MJPEG images, however, is often erroneous as the chrominace information is ...
Benny P. L. Lo, Guang-Zhong Yang
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A Neuro-Fuzzy Identification of ECG Beats
Journal of Medical Systems, 2010This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat.
Mohammed Amine Chikh +2 more
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On Designing of Neuro-Fuzzy Systems
2004This paper presents a new method to design the neuro-fuzzy systems. The procedure is composed of several separated techniques such as the WTA algorithm developed for fuzzy sets, learning from exceptions and the gradient learning for neuro-fuzzy systems.
Robert Nowicki +2 more
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Hierarchical Neuro-Fuzzy Systems
2003Robot audition in the real world should cope with environment noises and reverberation and motor noises caused by the robot's own movements. This paper presents the active direction-pass filter (ADPF) to separate sounds originating from the specified direction with a pair of microphones. The ADPF is implemented by hierarchical integration of visual and
Marley M. B. R. Vellasco +2 more
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Neuro-fuzzy Kolmogorov’s Network
2005A new computationally efficient learning algorithm for a hybrid system called further Neuro-Fuzzy Kolmogorov's Network (NFKN) is proposed. The NFKN is based on and is the development of the previously proposed neural and fuzzy systems using the famous superposition theorem by A.N. Kolmogorov (KST).
Yevgeniy V. Bodyanskiy +3 more
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Simplification of Neuro-Fuzzy Models
2009The neuro-fuzzy system presented in the paper is a system with parameterized consequences implementing hierarchical partition of the input domain. The regions are described with attributes values. In this system not all attribute values must be used to constitute the region. The attributes of minor importance may be ignored.
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