NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches

By Yang Gao, Meng Joo Er. 2005

246 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chin-Teng Lin
2. Yang-Yin Lin
3. Jyh-Yeong Chang
4. Kit Yan Chan
5. Eugen Diaconescu
6. Chia-Feng Juang
7. Tharam Singh Dillon
8. Wei Cheng

In Signal Processing

1. Kit Yan Chan
2. Eugen Diaconescu
3. Chin-Teng Lin
4. Chia-Feng Juang
5. Tharam Singh Dillon
6. Yao Qin
7. Yang-Yin Lin
8. Elizabeth J. Chang

Citations in the corpus

• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017, background): “To address this issue, various nonlinear autoregressive exogenous (NARX) models [Lin et al., 1996; Gao and Er, 2005; ∗Most of this work was performed while the first author was an intern at NEC Labs America.”
• The use of NARX neural networks to predict chaotic time series (2008, methodology): “1329-1351 [5] Yang Gao, Meng Joo Er, NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches, Fuzzy Sets and Systems, Vol.”
• A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning (2008, methodology): “based adaptive fuzzy system (RBF-AFS) [40], hybrid neural fuzzy inference system (HyFIS) [41], neurofuzzy function approximator (NEFPROX) [42], dynamic FNN (D-FNN) [43], a combination of the genetic algorithm and gradient descent algorithm (GEFREX) [44], subsethood-product fuzzy neural inference sys”
• Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network (2013, methodology): “Recently, the combination of recurrent structures and fuzzy neural networks has become popular in identifying and recognizing temporal behaviors [1]–[16].”
• An Aggregation Approach to Short-Term Traffic Flow Prediction (2009, methodology): “Using a hybrid model has become a common practice to improve forecasting accuracy [24], [25], and a combination of several models is employed in traffic flow forecasting [26]–[29].”
• Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, methodology): “A commonly used method for time series forecasting, i.e., TakagiSugeno fuzzy NNs [24], [25], [34], [36], which combine the mechanisms of fuzzy logic and feedforward NNs, has been proposed for short-term traffic flow forecasting [20], [39], [48].”