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General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model

2018 IEEE transactions on fuzzy systems  
This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno-Kang (TSK) or Mamdani  ...  Several benchmark data sets, including a problem of identification of a nonlinear system and a chaotic time series are considered.  ...  SIMPLIFIED GENERAL TYPE-2 RADIAL BASIS FUNCTION NEURAL NETWORK In this paper, a GT2 RBFNN that employs a directdefuzzification algorithm as an output layer is called Simplified General Type-2 Radial Basis  ... 
doi:10.1109/tfuzz.2018.2858740 fatcat:um6forwakjd7haetyli5slonzq

From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection [article]

Eyke Hüllermeier
2017 arXiv   pre-print
approach to fuzzy rule-based systems design by a data-driven one.  ...  It is argued that the classical rule-based modeling paradigm is actually more amenable to the knowledge-based approach, for which it has originally been conceived, while being less apt to data-driven model  ...  regression techniques such as radial basis function (RBF) networks.  ... 
arXiv:1712.00646v1 fatcat:775p5mkso5gu3ccbmz6qwicaoa

Fundamentals of Neural Networks

Amey Thakur
2021 International Journal for Research in Applied Science and Engineering Technology  
We also discuss different types of NNs and their applications. A brief introduction to Neuro-Fuzzy and its applications with a comprehensive review of NN technological advances is provided.  ...  Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs).  ...  A radial basis function neural network is seen in the diagram below.  ... 
doi:10.22214/ijraset.2021.37362 fatcat:2ebyvnxsj5djbewbd4ii4ubl4y

Forecasting electricity consumption in South Africa: ARMA, neural networks and neuro-fuzzy systems

Lufuno Marwala, Bhekisipho Twala
2014 2014 International Joint Conference on Neural Networks (IJCNN)  
The data was sampled on a monthly basis from January 1985 to December 2011.An ARMA,multilayer perceptron neural network with back propagation and neuro-fuzzy modelling technique which combines Takagi-Sugeno  ...  fuzzy models and neural networks were used to create the models for one step ahead forecasting.  ...  Hence, this approach is usually referredto as neuro-fuzzy modeling [14] . Under certain minor constraints the neuro-fuzzy architecture is also equivalent to a radial basis function network [13] .  ... 
doi:10.1109/ijcnn.2014.6889898 dblp:conf/ijcnn/MarwalaT14 fatcat:g5ijgmxrsffd3pwwo2rrk7jz2q

Interpretable Machine Learning: Convolutional Neural Networks with RBF Fuzzy Logic Classification Rules

Zhen Xi, George Panoutsos
2018 2018 International Conference on Intelligent Systems (IS)  
The classification layer is realised via a Radial Basis Function (RBF) Neural-Network, that is a direct equivalent of a class of Fuzzy Logic-based systems.  ...  Using simulation results on a benchmark data-driven modelling and classification problem (labelled handwriting digits, MNIST 70000 samples) we show that the proposed learning structure maintains a good  ...  RELATION TO EXISTING THEORIES AND WORK A. Radial Basis Function Neural-Fuzzy layer RBF networks were formulated in [14] as a learning network structure.  ... 
doi:10.1109/is.2018.8710470 dblp:conf/is/XiP18 fatcat:kz45t3h4azfufb6cd6zbhs4cya

A Hybrid Fault Diagnosis Approach for Hydraulic Systems by using Radial basis Function Networks

Xiang-yu He, Yijiao Yang, Shanghong He
2014 International Journal of Control and Automation  
To improve the reliability of hydraulic systems, a fault diagnosis approach based on radial basis function (RBF) networks was proposed in this paper.  ...  RBF networks were trained with data under a variety of fault conditions and then used for fault type classification on the hydraulic system.  ...  Radial Basis function (RBF) networks emerged as a variant of ANNs in the late 1980's.  ... 
doi:10.14257/ijca.2014.7.12.16 fatcat:wiqvaei3pfcillppyoc6547ol4

Prediction in Photovoltaic Power by Neural Networks

Antonello Rosato, Rosa Altilio, Rodolfo Araneo, Massimo Panella
2017 Energies  
In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference  ...  The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure  ...  Commonly-used types of radial basis functions include: • Gaussian: φ(r) = e −( r) 2 (6) • Multiquadric: φ(r) = 1 + ( r) 2 (7) • Inverse quadratic: φ(r) = 1 1 + ( r) 2 (8) Several methods can be used to  ... 
doi:10.3390/en10071003 fatcat:h636pfguefeavomdpxbjbxrhca

Classification Using Networks of Normalized Radial Basis Functions [chapter]

Guido Bugmann
1999 International Conference on Advances in Pattern Recognition  
Normalized Radial Basis Function Networks (NRBF) were invented at the same time as standard RBF nets, in 1989, but went unnoticed until recently, when it was found that they constitute a very interesting  ...  NRBF classifiers behave as Nearest Neighbour classifiers and have a functionality similar to Fuzzy Inference Systems but without the Curse of Dimensionality problem.  ...  The result on the Iris benchmark problem were produced during a student project by David Grant.  ... 
doi:10.1007/978-1-4471-0833-7_44 fatcat:imuzan7cincbfo6almbpg3pdga

Medical Image Segmentation Using Artificial Neural Networks [chapter]

Mostafa Jabarouti, Hamid Soltanian-Zadeh
2011 Artificial Neural Networks - Methodological Advances and Biomedical Applications  
Radial basis function network Radial Basis Function (RBF) neural network is another type of feed-forward neural network that uses radial basis functions as activation functions.  ...  The output of an RBF network is a linear combination of weighted radial basis functions.  ...  .), ISBN: 978-953-307-243-2, InTech, Available from:  ... 
doi:10.5772/16103 fatcat:jqghewpot5fkdofonq33e46rta

Ensemble of radial basis neural networks with k-means clustering for heating energy consumption prediction
Ansambl neuronskih mreža sa radijalno bazisnim funkcijama i k-means klasterizacijom za predviđanje potrošnje toplote

Radiša Jovanović, Aleksandra Sretenović
2017 FME Transaction  
Improvement of the predicton accuracy using k-means clustering for creating subsets used to train individual radial basis function neural networks is examined.  ...  For the prediction of heating energy consumption of university campus, neural network ensemble is proposed. Actual measured data are used for training and testing the models.  ...  Radial basis function networks (RBFN) A radial basis function network (RBFN), a type of feedforward neural network, consists of three layers including an input layer, a single hidden layer with a number  ... 
doi:10.5937/fmet1701051j fatcat:2ls7dbd7mfggfkkcvw4gnwuweu

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Balwinder S. Dhaliwal, Shyam S. Pattnaik
2013 Advances in Artificial Neural Systems  
Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields.  ...  The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed.  ...  An equilateral triangular microstrip antenna has been designed using a particle swarm optimization driven radial basis function neural networks by [8] .  ... 
doi:10.1155/2013/560969 fatcat:xxbxqu63b5hvhgev63pq3p6j3a

EEG-based mind driven type writer by fuzzy radial basis function neural classifier

Snehalika Lall, Anuradha Saha, Amit Konar, Mousumi Laha, Anca L. Ralescu, Kalyan kumar Mallik, Atulya K. Nagar
2016 2016 International Joint Conference on Neural Networks (IJCNN)  
Experiments undertaken reveal that the proposed type-2 fuzzy classifier outperforms both type-1 and traditional neural classifiers by a significant margin.  ...  Two models of fuzzy preprocessing are used. The first one is realized with type-1 fuzzy logic, whereas the second model is realized with interval type-2 fuzzy sets.  ...  type 2 fuzzy radial basis function (RBF) induced perceptron neural network (PNN).  ... 
doi:10.1109/ijcnn.2016.7727317 dblp:conf/ijcnn/LallSKLRMN16 fatcat:v5t3k7nnt5fs7fcgkl7q4pmr3i

A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran

Bashir Shokouh Saljoughi, Ardesir Hezarkhani
2018 Applied Geomatics  
The first map is created using a knowledge-driven fuzzy technique and the second one by a data-driven Artificial Neural Network (ANN) approach.  ...  Based on this study, the ANN technique is a better predictor of Cu mineralization compared to the fuzzy logic method.  ...  In the recent years, different types of neural network such as multi-layer perceptrons [28] , radial-basis function network [27] , probabilistic neural networks [30, 31] , and self-organizing map [  ... 
doi:10.1007/s12518-018-0229-z fatcat:xypl4nq7qzccdd7f57loentqsu

Data-Driven Student Learning Performance Prediction based on RBF Neural Network

Chunqiao Mi
2019 International Journal of Performability Engineering  
The activation functions of the hidden layer and output layer were a Gauss radial basis function and linear function, respectively.  ...  Secondly, driven by ten items of basic learning situation data, a learning performance prediction model based on the RBF neural network was established, which included three layers in network topology:  ...  Acknowledgments This study is supported by the project "Data-driven study on risk assessment and early warning of learning situation in Hunan Local Universities" (No. 17YBQ087), granted by the Hunan Provincial  ... 
doi:10.23940/ijpe.19.06.p7.15601569 fatcat:2vz3hoh57reb5nwf4kodesv6eq

Estimation of Soil Cation Exchange Capacity using Multiple Regression, Artificial Neural Networks, and Adaptive Neuro-fuzzy Inference System Models in Golestan Province, Iran

Hadi Ghorbani, Hamed Kashi, Naser Hafezi Moghadas, Samad Emamgholizadeh
2015 Communications in Soil Science and Plant Analysis  
Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) provide an alternative by estimating soil parameters from more readily available data.  ...  In this article, multilayer perceptron (MLP) and radial basis function (RBF) of ANN and ANFIS models were described to estimate soil cation exchange capacity and compared to traditional multiple regression  ...  The most used activation function is the sigmoid and it is given as follows: sgm (x) = 1 1 + e −x Radial Basis Function (RBF) Model Radial basis functions emerged as a variant of artificial neural network  ... 
doi:10.1080/00103624.2015.1006367 fatcat:ygpzqh2okzc5bgubaswnhqzbne
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