A Novel Hammerstein Model for Nonlinear Networked Systems Based on an Interval Type-2 Fuzzy Takagi–Sugeno–Kang System

Khalifa, Tarek R.; El-Nagar, Ahmad M.; El-Brawany, Mohamed A.; El-Araby, Essam A. G.; El-Bardini, Mohammad

doi:10.1109/tfuzz.2020.3007460

Cited by 24 publications

(6 citation statements)

References 32 publications

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“…fuzzy systems [19,20], extreme learning machine [21,22], neural fuzzy model [23][24][25], and long short-term memory network [26] have been reported in the literature to construct the static nonlinear block during past years. In [17], the new form of the Kolmogorov type neural network was used for identification of Hammerstein system, the algorithm of training the network is simple, well convergent and with a small error of approximation.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], the new form of the Kolmogorov type neural network was used for identification of Hammerstein system, the algorithm of training the network is simple, well convergent and with a small error of approximation. A novel Hammerstein structure where an interval type-2 Takagi-Sugeno-Kang fuzzy network and an autoregressive moving average are designed to model the nonlinear component and linear component, respectively, was developed for nonlinear networked systems, and updating techniques based on the Lyapunov theorem are implemented for estimating the Hammerstein model parameters [20]. Considering nonlinear approximation ability of extreme learning machine, a changing forgetting factor recursive least squares technique was carried out to identify extreme learning machine-based Hammerstein model [21].…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

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In this paper, we present a methodology for identifying the multi-input multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented is comprised of neural fuzzy models (NFM) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, i.e., the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function based least squares method in the presence of measurable input-output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is to introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input-output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a practical nonlinear process, and it is known from the research results, that compare with existing identification techniques, the methodology utilized achieved higher identification accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

View full text Add to dashboard Cite

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“…Moreover, based on ensemble learning, the sentiment analysis task was accomplished by combining the Bi-LSTM and Graph Convolutional Neural Network (GCN) techniques [27]. In addition, there are some works using fuzzy theory to perform the sentiment analysis [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols

Yang

Liu

et al. 2023

Electronics

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Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.

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“…This technique has been widely used in many fields, such as air quality evaluation, supplier assessment, etc. [33]. The detailed introduction is described as follows [34,35].…”

Section: Interval Type-ii Fuzzy Numbersmentioning

confidence: 99%

Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021

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With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

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A Novel Hammerstein Model for Nonlinear Networked Systems Based on an Interval Type-2 Fuzzy Takagi–Sugeno–Kang System

Cited by 24 publications

References 32 publications

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols

Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

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