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Chaotic Time Series Forecasting Using Higher Order Neural Networks
2016
International Journal on Advanced Science, Engineering and Information Technology
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This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. ...
Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). ...
Different types of ANN have been utilized for chaotic time series with varying degrees of success including Elman-Nonlinear Autoregressive with eXogenous input neural networks [2] , beta basis function ...
doi:10.18517/ijaseit.6.5.958
fatcat:uso5ldwbyran5altncy33gbpau
2019 Index IEEE Transactions on Fuzzy Systems Vol. 27
2019
IEEE transactions on fuzzy systems
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., +, TFUZZ Oct. 2019 1891-1903 General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model. ...
., +, TFUZZ May 2019 1112-1125 General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model. ...
Nonlinear filters Adaptive Neuro-Fuzzy Control for Discrete-Time Nonaffine Nonlinear Systems. Gil ...
doi:10.1109/tfuzz.2020.2966828
fatcat:pgfo5oksjrdbpa5s534ky74bie
IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation
[article]
2021
arXiv
pre-print
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In this paper, a new interval type-2 fuzzy neural network able to construct non-separable fuzzy rules with adaptive shapes is introduced. ...
Next, the new features are fed to a fuzzification layer using proposed interval type-2 fuzzy sets with adaptive shape. ...
using Gaussian membership function (GIT2FNN [9] ), an interval type-2 fuzzy neural network with beta basis function able to from fuzzy rules with different shapes (BIT2FNN [9] ), a recurrent interval ...
arXiv:2108.08704v1
fatcat:rnfmvztopzartby7r543ka4p74
Radial Basis Function in Artificial Neural Network for Prediction of Bankruptcy
2013
International Business Research
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Meanwhile, three neural networks of radial basis function type were built and trained separately by Altman model (1983 ( ), Zmijewski model (1984 and combinatory models' variables. ...
Prediction methods are constantly evolving, and artificial neural networks have nowadays found a special position among these methods. ...
There are different types of neural networks, One of the most important is Radial Basis Function. ...
doi:10.5539/ibr.v6n8p121
fatcat:dxsplumx7fez5cq6bvcojfdj7u
A Multi-Agent Architecture for the Design of Hierarchical Interval Type-2 Beta Fuzzy System
2019
IEEE transactions on fuzzy systems
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This work presents a new interval type-2 fuzzy system based on the Beta basis function [12] , [13] for system modeling. The proposed system is termed interval type-2 Beta fuzzy system (IT2BFS). ...
THE INTERVAL TYPE-2 BETA FUZZY SYSTEM
A. ...
doi:10.1109/tfuzz.2018.2871800
fatcat:gzjgded3xzclpmofyeqjktul7a
Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation
2022
Symmetry
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This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenarios. ...
Subsequently, numerical methods for uncertainty propagation are broadly reviewed under different computational strategies. ...
x ; ( ) i ψ ⋅ represents the radial basis function on i x ; i ω stands for the weight coefficient to be determined. A typical structure of RBF neural network is displayed in Figure 4. ...
doi:10.3390/sym14061219
fatcat:ofouvrkeiffgpltkm5dp5khyeu
Financial prediction and trading strategies using neurofuzzy approaches
1998
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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Neuro[uzzy approaches for predicting financial time series are investigated and shown to perfarm well in the context of various trading strategies. ...
The authors are also grateful Lo A. Ho bbs for sllbslaDtial conuiblllions Lo Lhe stock pricing model. ...
Typically, the input of a single time series into a neural network is made as shown in Figure 1 . ...
doi:10.1109/3477.704291
pmid:18255971
fatcat:4uunlrdmkzfvbeeqphpuqbw3f4
Predictions of the Apple Bruise Volume on the Basis of Impact Energy or Maximum Contact Force Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
2020
Acta Technologica Agriculturae
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However, its implementation time to reach a fixed error was longer. ...
In this research, adaptive neuro fuzzy inference system (ANFIS) was used to predict the bruise volume caused by the impacts on apples. ...
Adaptive neural network (ANFIS) as a basis for fuzzy inference systems The principle of ANFIS is based on fuzzy inference system (FIS) input/output data. ...
doi:10.2478/ata-2020-0019
fatcat:r3f5maurerhzzccvwf73bjfdlu
Prediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System
2021
International Journal of Advanced Trends in Computer Science and Engineering
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This study therefore presents the prediction of COVID-19 cases in South Africa and Egypt using interval type-2 fuzzy logic system with Takagi-Sugeno-Kang fuzzy inference and neural network learning. ...
The proposed model is found to outperform type-1 fuzzy logic system and artificial neural network in terms of the root mean squared error, mean absolute percentage error and mean absolute error ...
Reference [15] presented a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series in Mexico. COVID-19 data of Mexico was collected. ...
doi:10.30534/ijatcse/2021/241022021
fatcat:q5c6xxo3bnh5zbrnkmcthobh64
Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty
2021
Mathematical Problems in Engineering
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Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. ...
Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. ...
Huarng and Yu [28] study their extension of type-1 fuzzy time series models to type-2 models. ey designed a type-2 model for TAIEX index prediction. ...
doi:10.1155/2021/3839071
fatcat:fimkvsdrerhvdnq5hsvisbflty
Applications of neuro fuzzy systems: A brief review and future outline
2014
Applied Soft Computing
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AI methods are mainly comprised of fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro fuzzy systems, genetic fuzzy systems and genetic programming neural networks etc ...
For each of these categories, this paper mentions a brief future outline. ...
Acknowledgment The authors wish to express sincere gratitude to the anonymous reviewers for their constructive comments and helpful suggestions, which lead to substantial improvements of this paper. ...
doi:10.1016/j.asoc.2013.10.014
fatcat:iochb6rlgbhb5dmpmx7jeh54mu
Short term wind power forecasting using hybrid intelligent systems
2007
IEEE Power Engineering Society General Meeting
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This panel paper summarizes the current trends in wind power development and describes a proposed approach for short term wind power forecasting using a hybrid intelligent system. ...
These applications range from Expert Systems to assist with network fault diagnosis and rectification to Artificial Neural Networks and Fuzzy Logic to provide models for complex non-linear control problems ...
This innovative approach applies the combination of two Artificial Intelligence techniques, Fuzzy Logic and Artificial Neural Networks in the form of a hybrid model called an Adaptive Neural Fuzzy Inference ...
doi:10.1109/pes.2007.385453
fatcat:olqnsrxzxjcs7msykvggpkymrm
Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees
2008
Nuclear Fusion
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For these reasons, in the last years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking ...
On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive Fuzzy Logic predictor was trained and tested on JET database. ...
This approach of neural networks was used to predict the disruption boundary for the high-≤ disruption in DIII-D [2] . ...
doi:10.1088/0029-5515/48/3/035010
fatcat:oikjx6auefb23ojayi4z4quhhu
Short-term electrical load forecasting using a fuzzy ARTMAP neural network
1998
Applications and Science of Computational Intelligence
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The experiments showed that the Fuzzy ARTMAP architecture yields as accurate electrical load forecasts as a backpropagation neural network with training time a small fraction of the training time required ...
A neural network architecture that does not suffer from the above mentioned drawbacks is the Fuzzy ARTMAP neural network, developed by Carpenter, Grossberg, and their colleagues at Boston University. ...
Petersburg, Florida for the data provided for this research. ...
doi:10.1117/12.304804
fatcat:f46kstkmh5gr5icdtsmll5v5vi
A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system
2022
Electrical Engineering & Electromechanics
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This paper presents a Beta technique based MPPT controller to effectively track maximum power under all weather conditions. ...
The effectiveness of this algorithm based MPPT is supplemented by a comparative study with incremental conductance (INC), particle swarm optimization (PSO), and fuzzy logic control (FLC). ...
Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition. ...
doi:10.20998/2074-272x.2022.4.04
fatcat:ajwfftlq3jfnzhna2fjn7nwkwm
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