DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

By Nikola K. Kasabov, Q. Song. IEEE transactions on fuzzy systems, 2002

1,299 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/67045a715cf61132d0ffc96ae43fb9dfdf1227bf

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In Artificial Intelligence

Citations in the corpus

Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images (2008, methodology): “To improve the performance in detecting LSB matching steganography in grayscale images, based on our previous work [16], in this paper, we propose five types of features and introduce a dynamic evolving neural fuzzy inference system (DENFIS) [18,19].”
Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification (2020, methodology): “We first use 5000 noisy training data to train fuzzy BLS and BLS, and compare their performance with ANFIS [38], SAFIS [39], eTS [40], Simpl_eTS [41], DENFIS [42], and OS-F-ELM [22].”
Prediction of Suspended Sediment Load Using Data-Driven Models (2019, methodology): “The iteration process continues as long as reaching to a predetermined threshold value: maxi j ( ui jk+1 − ui jk ) ε (7) DENFIS, which was first presented by Kasabov and Song [41], is a FIS model that uses clustering procedure for online and offline learning.”
NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches (2005, methodology): “Now, a lot of NN/FNN learning algorithms also offer structure learning, using techniques such as genetic algorithms [9,14], orthogonal techniques [3,17], similarity/degree measure [2,12], and various integrated one-pass methods [4,10,11,16,19], etc.”
On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers (2012, methodology): “T1 FS theory was first introduced by Zadeh [66] in 1965 and has been successfully applied in many areas, including modeling and control [6], [47], [65], data mining [22], [40], [69], timeseries prediction [27], [29], [45], linguistic summarization [24], [54], [64], computing with words [41], [42], [”
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, methodology): “The precise NFS used in this paper is the Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) [31].”
An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, methodology): “0 as suggested in previous two studies [42], [43].”
Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction (2006, methodology): “In order to compare with the results of [12], the observations between t = 201 and 3200 and the observations between t = 5001 and 5500 are extracted from the series and used as training and testing data.”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, methodology): “5) Dynamic evolving neural-fuzzy inference system (DENFIS) [ 36 ].”
Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, background): “1) Hybrid NN approaches, which incorporate other computational intelligence methods or statistical prediction methods that have been recently investigated for enhancing the generalization capabilities of NNs.”
An approach to online identification of Takagi-Sugeno fuzzy models (2004, methodology): “It is interesting to note that the rate of upgrade with new rules does not lead to an excessively large rule base in comparison to [15], [20], [27], [31].”
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, background): “A promising approach to deal with large amounts of data is through the use of a class of methods known as evolving intelligent systems [10-14].”
SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network (2009, methodology): “The system identiﬁcation can be classiﬁed into two groups: 1) ofﬂine identiﬁcation [4], [9], [18], [19], [30], [39], [42] and 2) online identiﬁcation [2], [3], [20], [21], [23], [24], [27], [28], [40], [41], [43].”
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning (2008, methodology): “The dynamic evolving neural fuzzy inference system (DENFIS) proposed in [18] used a general Euclidean distance as an online rule generation criterion.”
Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems (2009, methodology): “They include dynamic evolving neurofuzzy inference system (DENFIS) [4], evolving Takagi–Sugeno (eTS) model [5], Simplified eTS (Simpl_eTS) algorithms [6], and sequential adaptive fuzzy inference system (SAFIS) [7].”
Evolving Fuzzy-Rule-Based Classifiers From Data Streams (2008, methodology): “" This paradigm has been applied to NNs [31] and fuzzy systems [26]–[30].”