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A wavelet-based recurrent fuzzy neural network trained with stochastic optimization algorithm
2009
2009 IEEE International Conference on Systems, Man and Cybernetics
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this paper presents a Wavelet-based Recurrent Fuzzy Neural Networks (WRFNN) trained with a stochastic searchbased adaptation algorithm. ...
A WRFNN represents a recurrent network of neurons employing wavelet functions whose outputs are combined using fuzzy rules. ...
In section 2 the model of wavelet neural networks will be described. In section 3 structure of the wavelet-based recurrent fuzzy neural network model will be given. ...
doi:10.1109/icsmc.2009.5346702
dblp:conf/smc/AbdulsaddaI09
fatcat:mb3lfvguy5a4zi2xxrffrljdgm
Recurrent wavelet-based neuro fuzzy networks for dynamic system identification
2005
Mathematical and computer modelling
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A recurrent wavelet-based neuro fuzzy network (RWNFN) is proposed in this paper. The proposed RWNFN integrates wavelet transforms with fuzzy rules. ...
Temporal relations are embedded in the network by adding feedback connections from memory units in the third layer of a feedforward wavelet-based neuro fuzzy network (WNFN). ...
In this paper, we propose a recurrent wavelet-based neuro fuzzy network (I~WNFN). The RWNFN model is based on our previous research [2] . ...
doi:10.1016/j.mcm.2004.05.004
fatcat:mudnnek3vvdv3gvyaa2s7nmgfq
Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network
2019
2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)
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In this paper, the integration of Type-2 fuzzy set theory and recurrent wavelet neural network(WNN) is proposed to allow managing of nonuniform uncertainties for identifying non-linear dynamic system. ...
The structure has both advantages of recurrent and wavelet neural network which expand the basic ability of fuzzy neural network to deal with temporal problems. ...
In this study, a type-2 fuzzy recurrent wavelet neural network(T2FRWNN) is presented which integrates FWNN and recurrent neural network to benefits the advantages of both. ...
doi:10.1109/cfis.2019.8692153
fatcat:k3egrwqsh5g5nbbz3jmfqwqlg4
Identification and Control of Dynamic Plants Using Fuzzy Wavelet Neural Networks
2008
2008 IEEE International Symposium on Intelligent Control
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This paper presents a Fuzzy Wavelet Neural Network (FWNN) for identification and control of a dynamic plant. ...
The FWNN is constructed on the basis of fuzzy rules that incorporate wavelet functions in their consequent parts. ...
Some of the neuro-fuzzy structures proposed utilize recurrent neural networks. In [8] a recurrent fuzzy network is used for nonlinear modelling. ...
doi:10.1109/isic.2008.4635940
dblp:conf/IEEEisic/AbiyevK08
fatcat:tsaz6cksjrh3hc2mfyqejevwza
Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study
2008
IEEE transactions on industrial electronics (1982. Print)
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In this paper, the integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem. The proposed fuzzy WNN is constructed on the base of a set of fuzzy rules. ...
Index Terms-Control, fuzzy wavelet neural network (FWNN), identification, wavelet. ...
Some of the neuro-fuzzy structures proposed utilize recurrent NNs. In [8] , a recurrent fuzzy network is used for nonlinear modeling. ...
doi:10.1109/tie.2008.924018
fatcat:v6ef2lh3h5a3dnufyu6efqbiqq
A Self-Organizing Recurrent Wavelet Neural Network for Nonlinear Dynamic System Identification
2015
Applied Mathematics & Information Sciences
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To solve identification of nonlinear dynamic systems, a recurrent wavelet neural network (RWNN) model is proposed in this paper. The proposed RWNN model has four-layer structure. ...
The structure learning is based on the input partitions to determine the number of wavelet bases, and the parameter learning is based on the supervised gradient descent method to adjust the shape of wavelet ...
Conclusion In this paper, we propose a recurrent wavelet neural network (RWNN) model for solving temporal problems. We adopt the orthogonal function as wavelet neural network bases. ...
doi:10.12785/amis/091l16
fatcat:mdjxdr75bfaezjxromwfybnymi
Neurocomputing in Civil Infrastructure
2016
Scientia Iranica. International Journal of Science and Technology
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In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural ...
The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. ...
\Dynamic fuzzy wavelet
neural network model for structural system identi-
cation", Journal of Structural Engineering, 132(1),
pp. 102-111 (2006).
29. Jiang, X. and Adeli, H. ...
doi:10.24200/sci.2016.2301
fatcat:f35gtppgofaojkbertgsowsi24
FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods
2021
Computational Intelligence and Neuroscience
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In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. ...
For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. ...
In addition, the fuzzy neural network's additional features such as time-series prediction [18] , identification of nonlinear dynamical systems [12] , dynamic fuzzy wavelet neural network [19] , function ...
doi:10.1155/2021/8542637
pmid:34853586
pmcid:PMC8629672
fatcat:7eagtcl7m5fozmot3x3vrnprve
On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory
2004
Neural computing & applications (Print)
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The structure of dynamic networks are based on Hopfield networks. Here, we present a comparative study of DNNs, DFNs, and DWNs for non-linear dynamical system modeling. ...
These problems can be overcome with dynamic network structures, referred to as dynamic neural networks (DNNs), dynamic fuzzy networks (DFNs), and dynamic wavelet networks (DWNs), which have unconstrained ...
fuzzy networks (DFNs), and those based on dynamic wavelet networks (DWNs). ...
doi:10.1007/s00521-004-0429-9
fatcat:6z4xyx5fprh2xobhozecctosqm
Application of a Self-recurrent Wavelet Neural Network in the Modeling and Control of an AC Servo System
2014
Sensors & Transducers
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A self-recurrent wavelet neural network (SRWNN) modeling scheme is proposed, which successfully addresses the issue of the traditional wavelet neural network easily falling into local optimum, and significantly ...
The control scheme of a SRWNN based on fuzzy compensation is expected. ...
As a feed forward network proposed on the basis of wavelet analysis, wavelet neural network effectively combines the structural model of a neural network with the multi-resolution and multi-scale analysis ...
doaj:1f29c20a478b4b42bfdccc81b524efea
fatcat:zaggvhadljdbbjrghicyiawf6y
Adaptive Tracking Control Based on Recurrent Wavelet Fuzzy CMAC for Uncertain Nonlinear Systems
2018
International Journal of Control and Automation
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This paper presents a control system based on the recurrent wavelet fuzzy cerebellar model articulation controller (RWFCMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems ...
The proposed control system is applied to imitate an ideal controller because it incorporates the advantages of the wavelet decomposition property with a fuzzy CMAC fast learning ability and an adaptive ...
The fuzzy neural networks have been proposed by combining a fuzzy rule base system with and a neural network [2, 3] . ...
doi:10.14257/ijca.2018.11.1.07
fatcat:uygpzo7v2jcsxah2pp2dvkduia
Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications
2019
PLoS ONE
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In this paper, we proposed a new FWNN model nominated "Fuzzy Jump Wavelet Neural Network" (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. ...
Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. ...
Acknowledgments The authors are grateful to the Laboratory of Engineering of Neuromuscular System and Motor Rehabilitation, Politecnico di Torino for the recording of the EMG-Torque data. ...
doi:10.1371/journal.pone.0224075
pmid:31816627
pmcid:PMC6901348
fatcat:fpge6n3yvfbbph4hpe75ailnsq
Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network
2013
IEEE Transactions on Neural Networks and Learning Systems
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This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. ...
Index Terms-Dynamic sequence prediction, fuzzy identification, on-line fuzzy clustering, recurrent fuzzy neural networks. ...
IEEE self-evolving fuzzy neural network (IRSFNN), for dynamic system identification and prediction. ...
doi:10.1109/tnnls.2012.2231436
pmid:24808284
fatcat:jpyx2lxwavajpkrdnkm72dgs4a
Recurrent Fuzzy Wavelet Neural Network to Control a PCV
2015
Indian Journal of Science and Technology
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In this paper, a Recurrent Fuzzy Wavelet Neural Network (RFWNN) is constructed by using Recurrent Wavelet Neural Network (RWNN). ...
In RWNN, temporal relations are embedded in the network by adding feedback connections on the first layer of the network, and wavelet basis n is used as fuzzy membership function. ...
Recurrent fuzzy neural network 18, 19 is amodified type of recurrent neural network, which uses recurrent neural network for realizing fuzzy inference. ...
doi:10.17485/ijst/2015/v8i36/53072
fatcat:lukz4aug5nhvngid4ifmj76cqa
Three Dimensional Surface Reconstruction Method for the Welding Pool Using the Fuzzy Neural Network
2016
Chemical Engineering Transactions
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As the relationships between size and shape of the welding pool are very complex and nonlinear, we utilize the fuzzy neural network to solve the proposed problem. ...
In the fuzzy neural network, the input vector with 48 dimensions is made up of three parts: 1) welding parameters, 2) welding pool size parameters, and 3) shape parameters. ...
Based on the above definitions, the details of the fuzzy neural network model are explained as follows (1) Input nodes layer In this layer, each node is regarded as an input variable, and the node only ...
doi:10.3303/cet1655035
doaj:e6b05f31d9f04a639ea200232976e546
fatcat:qabdb2yyqzaqrk4k2yrd3t3jkq
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