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Backpropagation method with type-2 fuzzy weight adjustment for neural network learning
2012
2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)
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In this paper a neural network learning method with type-2 fuzzy weight adjustment is proposed. ...
In this work an ensemble neural network of three neural networks and average integration for obtain the final result is present. The proposed approach is applied to a case of time series prediction. ...
The best result with the ensemble neural network with type-2 fuzzy weights for the Mackey-Glass time series is a prediction error of 0.0788 (as shown in Fig.6 and Table 1 ). ...
doi:10.1109/nafips.2012.6291056
fatcat:bvkqq6rm2zbe7egofivdqsjka4
Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks
2017
Information
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The interval type-2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. ...
The adjustment of the weights in the backpropagation learning using interval type-2 fuzzy numbers is the main contribution of the proposed work in this paper for neural networks. ...
. 2 Results for the fuzzy neural network with interval type-2 fuzzy numbers with T-norm of Dombi in time series prediction using Mackey-Glass time series. ...
doi:10.3390/info8030114
fatcat:jnsxz3tnr5cyfg2mogbup7d22e
Hybrid system prediction for the stock market: The case of transitional markets
2017
Industrija
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The subject of this paper is the creation and testing of an enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes, including the comparison with the traditional ...
The objective of the research is to gather information concerning the possibilities of using the enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes focusing ...
Introduction This paper proposes adjustment of weights in backpropagation model for neural networks by implementing fuzzy logic. ...
doi:10.5937/industrija45-11052
fatcat:z5oiqfdlkjfv3aant4iactyhdu
ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster
2015
The Scientific World Journal
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The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. ...
We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. ...
For the iteration , the weight change Δ can be expressed. The backpropagation learning algorithm used in artificial neural networks is shown in many text books [3] [4] [5] [6] .
Fuzzy Logic. ...
doi:10.1155/2015/205749
pmid:26221620
pmcid:PMC4499654
fatcat:mu7ua5wxfra6noemt7szkzsjva
Daily Weather Forecasting using Artificial Neural Network
2015
International Journal of Computer Applications
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Among which neural network with the backpropagation algorithm performs prediction with minimal error. Neural network is a complex network which is self-adaptive in nature. ...
, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, Fuzzy clustering, etc. which are used for different types of forecasting. ...
A Neuro evaluative Interval Type -2 TSK Fuzzy
System
5. Grey Relational Analysis. ...
doi:10.5120/21830-5088
fatcat:nb5lyzmtkffgtajcbpchyimfhq
Integration of connectionist methods and chaotic time-series analysis for the prediction of process data
1998
International Journal of Intelligent Systems
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A connectionist-based time-series analysis method is described that includes chaotic characterization, fractal analysis together with statistical data processing in an adaptive Ž . fuzzy neural network ...
Two major aspects of the present work are 1 incorporating knowledge into the fuzzy neural network based on the nonlinear deterministic, chaotic analysis of the signals and Ž . 2 refining and updating the ...
In the present study, a special type of network pruning is applied, which is a modified backpropagation learning algorithm with forgetting the connection weights. ...
doi:10.1002/(sici)1098-111x(199806)13:6<519::aid-int7>3.0.co;2-o
fatcat:37k3zn3k55bvbmhwr3kohylema
Fuzzy adaptive learning control network with on-line neural learning
1995
Fuzzy sets and systems (Print)
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The proposed Fuzzy Adaptive Learning COntrol Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. ...
The FALCON-ART combines the backpropagation learning scheme for parameter learning and a fuzzy ART algorithm for structure learning. ...
First, one can use a neural network with its neural learning ability as a separate system from the fuzzy system for automatic determination and adjustment of fuzzy rules and/or membership functions. ...
doi:10.1016/0165-0114(94)00195-d
fatcat:43omrfbfurgm5efagekyw64o3m
Intelligent control of air servodrives using neural networks
1993
Proceedings of the JFPS International symposium on fluid power
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Two popular methods for implementing intelligent control, namely fuzzy set theory and neural networks, are discussed. ...
Current work involving the application of neural networks to air servodrive systems, is introduced. ...
Backpropagation is the standard method used to adjust the weights of the neural net. Hence the error, the difference between y(t) and 9(t), is used for adjusting the weights. ...
doi:10.5739/isfp.1993.743
fatcat:e4ta5st5tvf4nmvt2ca7f2sq7a
Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems
2021
IEEE Access
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In this work, a review of the state-of-the-art of methodologies for prediction interval modelling based on fuzzy systems and neural networks is presented. ...
INDEX TERMS Prediction intervals, fuzzy interval, neural network intervals, uncertainty. ...
INTRODUCTION The use of fuzzy logic systems (FLS) and neural networks (NNs) has proliferated in the literature for the modeling of systems and time series. ...
doi:10.1109/access.2021.3056003
fatcat:tzifk4mofjezdedhayj4vlnrqq
A Comprehensive Review of Soft Computing Models for Permeability Prediction
2020
IEEE Access
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In this paper, we present an extensive review of the existing research that has been conducted on applications of soft computing for permeability prediction. ...
This paper finds out that traditional approaches for permeability prediction are still relevant in the oil and gas industry. ...
It has been applied in several applications such as data mining, time series modeling, reservoir characterization, and permeability prediction. ...
doi:10.1109/access.2020.3046698
fatcat:az4zmfyd5fa4lnk4nxpglmy7fa
POTENTIAL AREA MAPPING FOR SEAWEED AQUACULTURE BASED ON INTERVAL TYPE-2 FUZZY SETS AND MULTI-LAYER PERCEPTRON ALGORITHM
2019
International Journal of Advanced Research
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This paper proposes a mapping model for seaweed aquaculture based on Interval Type-2 Fuzzy Sets (IT2FS) and Multi-Layer Perceptron (MLP) algorithm as a new framework to map potential area for seaweed cultivation ...
The decision output from IT2FS is potential or not potential with specific prediction of production size for each region. ...
The results of this prediction are used in the next stage to predict the availability of seaweed. This stage is called the time series learning stage for attribute predictions. ...
doi:10.21474/ijar01/8584
fatcat:7lc5ki5dj5c4fgbaoe6bx4jisi
Neurocontroller alternatives for "fuzzy" ball-and-beam systems with nonuniform nonlinear friction
2000
IEEE Transactions on Neural Networks
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We have used truncated backpropagation through time with the Node-Decoupled Extended Kalman Filter (NDEKF) algorithm to update the weights in the networks. ...
The ball-and-beam problem is a benchmark for testing control algorithms. In the World Congress On Neural Networks, 1994, Prof. ...
The set of matrices H i in (1), (2), (4) is obtained by truncated backpropagation through time with depth 10. ...
doi:10.1109/72.839012
pmid:18249772
fatcat:sqiqb2uspbblxi2aszemezjiry
Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network
2018
Applied Soft Computing
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neural networks converge significantly faster than backpropagation neural networks (improved benchmark in precision). ...
., artificial neural networks (ANNs) [4], fuzzy logic [5], etc. However, multinodal prediction is complex in comparison to global prediction. ...
Acknowledgment The authors thank CAPES (Brazilian Research Foundation) and CNPq (National Council for Scientific and Technological Development) for financial support. ...
doi:10.1016/j.asoc.2018.06.039
fatcat:o6myurjjmvdmvggokvwemmkunq
Neurocontrol and fuzzy logic: Connections and designs
1992
International Journal of Approximate Reasoning
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Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ...
For example, one can learn rules in a hybrid fashion and then calibrate them for better whole-system performance. ...
Then, we can use generalized backpropagation directly to adjust the weights (or uncertainty levels or other parameters) in that network. ...
doi:10.1016/0888-613x(92)90017-t
fatcat:ebkzw6hoejeldhginfnvtxz2pi
Comparison of BPA and LMA Methods for Takagi - Sugeno type MIMO Neuro-Fuzzy Network to Forecast Electrical Load Time Series
2008
Jurnal Teknik Elektro
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Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of Electrical load time series. ...
This paper describes an accelerated Backpropagation algorithm (BPA) that can be used to train the Takagi-Sugeno (TS) type multi-input multi-output (MIMO) neuro-fuzzy network efficiently. ...
application of electrical load time series. ...
doi:10.9744/jte.7.2.101-109
fatcat:bvnx3c3ex5bibmqvydzvasp2dq
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