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Learning in Deep Radial Basis Function Networks [PDF]
EntropyLearning in neural networks with locally-tuned neuron models such as radial Basis Function (RBF) networks is often seen as instable, in particular when multi-layered architectures are used. Furthermore, universal approximation theorems for single-layered
Fabian Wurzberger, Friedhelm Schwenker
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Efficient VLSI Architecture for Training Radial Basis Function Networks [PDF]
Sensors, 2013 This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations.
Wen-Jyi Hwang, Zhe-Cheng Fan
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Radial Basis Function Cascade Correlation Networks [PDF]
Algorithms, 2009 A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard
Peter de B. Harrington, Weiying Lu
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Radial basis function neural networks: a topical state-of-the-art survey
Open Computer Science, 2016 Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors.
Dash Ch. Sanjeev Kumar +3 more
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RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control
Sensors, 2021 Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks.
Dongxi Zheng, Wonsuk Jung, Sunghoon Kim
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Estimation of Groundwater Seepage Risks into Tunnel Using Radial Basis Function Networks [PDF]
علوم و مهندسی آبیاری, 2022 In this study, Site Groundwater Rating (SGR) in the Amirkabir tunnel has been estimated using Radial Basis Function Networks (RBFNs). SGR is the first rating method that by considering the parameters like joint frequency, joint aperture, schistosity ...
Hadi Farhadian, Seyed Ahmad Eslaminezhad
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Chess Position Evaluation Using Radial Basis Function Neural Networks
Complexity, 2023 The game of chess is the most widely examined game in the field of artificial intelligence and machine learning. In this work, we propose a new method for obtaining the evaluation of a chess position without using tree search and examining each candidate
Dimitrios Kagkas +2 more
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Snow cover thickness estimation using radial basis function networks [PDF]
The Cryosphere, 2013 This paper reports an experimental study designed for the in-depth investigation of how the radial basis function network (RBFN) estimates snow cover thickness as a function of climate and topographic parameters.
E. Binaghi +3 more
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Comparison between Wavelet and Radial Basis Function Neural Networks for GPS Prediction [PDF]
Engineering and Technology Journal, 2015 Neural networks are complex nonlinear models;this characteristic enables them to be used in nonlinear system modeling and prediction applications.The estimation and prediction are importantroles in the communication system.The proposed approach based ...
Farag Mahel Mohammed +2 more
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Decision Feedback Equalizers Using Radial Basis Function Networks
Journal of King Saud University: Engineering Sciences, 2000 Decision feedback equalizers (DFE)s are used extensively in practical communication systems. They are more powerful than linear equalizers especially for severe inter-symbol interference (ISI) channels with deep frequency null.
S.A. Zummo, A. Balghonaim, M. Mohandes
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