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A recurrent self-organizing neural fuzzy inference network
1999
IEEE Transactions on Neural Networks
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A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. ...
The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. ...
In this paper, we shall propose such a recurrent neural fuzzy network. ...
doi:10.1109/72.774232
pmid:18252581
fatcat:2dymxeot5bdhzbhzdv2ublzjui
Table of contents
2019
2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)
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1 Extracting The Best Features For Predicting The Trend Of Tehran Stock Exchange (TSE) 6 Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network 75 Application of Fuzzy Inference ...
Wavelet Transform
31
Relevant Question Answering in Community Based Networks Using Deep LSTM Neural Networks
36
New Concepts of Matching in Fuzzy Graphs
41
A Fuzzy Directional Median Filter for ...
doi:10.1109/cfis.2019.8692170
fatcat:vuckgu24gnezthr5lxel7m2oxe
Prediction of Facebook Post Metrics using Machine Learning
[article]
2018
arXiv
pre-print
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In this article, we analyze the efficiency for predicting the post impact of three popular techniques: Support Vector Regression (SVR), Echo State Network (ESN) and Adaptive Network Fuzzy Inject System ...
In this short paper, we evaluate the performance of three well-known Machine Learning techniques for predicting the impact of a post in Facebook. ...
Adaptive Network Fuzzy Inject System The Adaptive-Network-Fuzzy Inject System (ANFIS) AN-FIS is the abbreviation Adaptive-Network-Fuzzy Inject System -an adaptive network of fuzzy output. ...
arXiv:1805.05579v1
fatcat:iudn3wxsqzcxngrepko5csq5aq
A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network
2016
Mathematical Problems in Engineering
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A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature. ...
In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN). ...
In particular, recurrent neural network (RNN) has been proved successful in speech processing and adaptive channel equalization. ...
doi:10.1155/2016/3542898
fatcat:s6ramypnajc7vb7lg4fxkh7cqy
Guest Editors' Introduction: Neural Networks For Signal Processing
1997
IEEE Transactions on Signal Processing
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In "Fast Adaptive Digital Equalization by Recurrent Neural Networks," the authors develop a discriminative learning algorithm for recurrent neural networks. ...
In "Neural-Fuzzy Classification for Segmentation of Remotely Sensed Imageries," remote sensing image segmentation is accomplished using an adaptive resonance theory (ART) classifier, followed by a clustering ...
doi:10.1109/tsp.1997.650089
fatcat:se3mm23wzna73iwb6to666b2re
ANFIS for Tamil Phoneme Classification
2019
International Journal of Engineering and Advanced Technology
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neural networks, time delay neural networks, etc. for efficient phoneme recognition. ...
In this paper, we study the effectiveness of the hybrid architecture, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for capturing the spectral features of the speech signal to handle the problem of ...
A direct approach that combines a sequence of processes acoustic modeling, language modeling and sequence prediction in continuous speech recognition is developed using Recurrent Neural Networks (RNN) ...
doi:10.35940/ijeat.f8804.088619
fatcat:rgefl6a26jcgjmqu2u4cdlcyh4
Page 201 of Neural Computation Vol. 8, Issue 1
[page]
1996
Neural Computation
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
A learning algorithm for continually running fully recurrent neural networks. Neural Comp. 1, 270-280.
Received April 29, 1994; accepted May 16, 1995. ...
Acoustics, Speech, Signal Process. 37(3), 328-339.
Wan, E. 1993a. Finite impulse response neural networks with applications in time series prediction. Ph.D. dissertation. ...
Guest Editorial Special Section on Soft Computing in Industrial Informatics
2012
IEEE Transactions on Industrial Informatics
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He is currently serving as Officer for the IEEE Industrial Electronics Society, chairing technical committee on resilience and security for industrial applications, and is involved in various capacities ...
He has lead the Computer Science Program at the University of Idaho and is a Director of the Modern Heuristics Group. ...
The paper "Model Predictive Control of Nonlinear Systems with Unmodeled Dynamics Based on Feedforward and Recurrent Neural Networks," by Wang and Yan, develops a model predictive control for dynamical ...
doi:10.1109/tii.2012.2215335
fatcat:6huy7iekabfv7pvgp4ywl6toqe
2020 Index IEEE Transactions on Cognitive and Developmental Systems Vol. 12
2020
IEEE Transactions on Cognitive and Developmental Systems
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A Acevedo-Valle, J.M., Hafner, ...
in Neural Networks; Yuan, C., Xia, Z., Sun, X., and Wu, Q.M.J., Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection; TCDS Sept. 2020 461-473 Yuan, H., seeYu, X., ...
., +, TCDS March 2020 73-83 Speech enhancement Ensemble Hierarchical Extreme Learning Machine for Speech Dereverberation. ...
doi:10.1109/tcds.2020.3044690
fatcat:yfo6c366aramfdltqegqyqphbq
Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model
2021
Mathematical Problems in Engineering
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The proposed model takes advantage of type-2 recurrent wavelet fuzzy theory and brain emotional neural network. ...
The system input data streams are directly imported into the neural network through a type-2 recurrent wavelet fuzzy inference system; then, the results are subsequently piped into sensory and emotional ...
[14] expanded the conditional probability neural network to a fuzzy form, which is used to predict the emotions expressed by the type-2 recurrent wavelet fuzzy brain emotional learning network can be ...
doi:10.1155/2021/9991531
fatcat:szbyyfn7uvfhbjy2auxnodu2lu
NNGD algorithm for neural adaptive filters
2000
Electronics Letters
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However, we believe that, although we have presented just a heuristic approach to such a design, its strength lies in the fact that it opens new research possibilities towards finding more effective design ...
function and the learning rate within a recurrent neural network', Neural Comput., 1999, 11, (9, pp. 1069-1077 MANDIC, D.P., and CHAMBERS, J.A.: 'Towards an optimal learning rate for backpropagation', ...
In another experiment, the performance of neural adaptive algorithms was compared with the performances of the LMS and NLMS algorithms for a speech signal s2. ...
doi:10.1049/el:20000631
fatcat:cc7plltjmreqdgqio7hhf6dtcy
2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25
2014
IEEE Transactions on Neural Networks and Learning Systems
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Class of Predictive Networks; TNNLS Oct. 2014 1921-1927 Zhang, H., Wang, Z., and Liu, D., A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks; TNNLS Jul. 2014 1229 ...
., +, TNNLS Apr. 2014
664-676
Fuzzy neural networks
A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System. ...
The Field of Values of a Matrix and Neural Networks. Georgiou, G.M., TNNLS Sep. 2014 ...
doi:10.1109/tnnls.2015.2396731
fatcat:ztnfcozrejhhfdwg7t2f5xlype
Computational Intelligence in Multimedia Processing: Foundation and Trends
[chapter]
2008
Studies in Computational Intelligence
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This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough ...
A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and ...
Major neural networks that are commonly used for multimedia applications are classified as feed-forward neural network, feedback network or recurrent, self-organizing map and Adaptive Resonance Theory ...
doi:10.1007/978-3-540-76827-2_1
fatcat:7jl4ir66vvcl7g436ektwkqwpy
Assessment of Parkinson's disease progression using neural network and ANFIS models
2016
Neural Network World
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Computational methods considered in this paper are artificial neural networks, particularly feedforward networks with several variants of backpropagation learning algorithm, and adaptive network-based ...
fuzzy inference system (ANFIS). ...
As for the speech recognition in general, the modern attitudes tend to apply deep bidirectional Long Short-Term Memory recurrent neural networks and deep neural networks [13, 18] . ...
doi:10.14311/nnw.2016.26.006
fatcat:ksctcw5j6bdfjpf5rwitpww6fm
The neural paradigm for complex systems: new algorithms and applications
2011
Neural computing & applications (Print)
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Wang et al., the global asymptotic stability problem for a class of recurrent neural networks with multi-time scale is studied. ...
Wang et al. propose a novel neural network-based iterative adaptive dynamic programming (ADP) algorithm for solving the optimal control problem of a class of nonlinear discrete-time systems with control ...
doi:10.1007/s00521-011-0713-4
fatcat:ww5yp7ewd5bs7muhoeos4ip4wu
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