Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system

Eyoh, Imo; John, Robert; Maere, Geert De

doi:10.1109/smc.2017.8122694

Cited by 10 publications

(10 citation statements)

References 26 publications

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“…The parameters of the IT2IFNN in [9] are optimized using EKF (a second-order derivative-based method). In [10], the DEKF is adopted for the optimization of the parameters of the IT2IFLS. Using the DEKF allows the parameters of the model to be grouped into vectors such as antecedent and consequent vectors so that interactions are allowed at the second order.…”

Section: Related Workmentioning

confidence: 99%

“…According to [25], the NSW electricity market in the year 2008 was chosen for the analysis because it was the largest. In [10], the same dataset was analysed using IT2IFLS trained with the decoupled extended Kalman filter algorithm. To aid comparison with other learning algorithms, the computational arrangement is the same as that used in [10] with 336 input data for each season,…”

Section: Australian New Electricity Marketmentioning

confidence: 99%

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2020

IJFLS

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This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models. KEYWORDSInterval type-2 intuitionistic fuzzy set, Sliding mode control algorithm, Intuitionistic fuzzy set, Intuitionistic fuzzy index.

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Section: Related Workmentioning

confidence: 99%

Section: Australian New Electricity Marketmentioning

confidence: 99%

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2020

IJFLS

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“…For IT2IFLS-TSK, the antecedent are IT2IFSs while the consequent parts are linear combinations of the inputs. A typical rule structure of IT2IFLS is as shown in (14).…”

Section: Rulesmentioning

confidence: 99%

“…The developed model in [12] is applied for non-linear system prediction with encouraging results. The same authors in Eyoh et al [13,14] applied the IT2IFLS framework for time series prediction. Results reveal that IT2IFLS exhibits superior performance to many non-fuzzy and some fuzzy approaches.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Intuitionistic Fuzzy Logic Systems - A Comparative Evaluation

Eyoh

John

Maere

2018

Communications in Computer and Information Science

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Abstract. Several fuzzy modeling techniques have been employed for handling uncertainties in data. This study presents a comparative evaluation of a new class of interval type-2 fuzzy logic system (IT2FLS) namely: interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK)-type with classical IT2FLS and its type-1 variant (IFLS). Simulations are conducted using a real-world gas compression system (GCS) dataset. Study shows that the performance of the proposed framework with membership functions (MFs) and nonmembership functions (NMFs) that are each intervals is superior to classical IT2FLS with only MFs (upper and lower) and IFLS with MFs and NMFs that are not intervals in this problem domain.

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“…Subsequently, in [37], an interval type-2 intuitionistic fuzzy logic system of Takagi-Sugeno-Kang (IT2IFLS-TSK) was proposed to achieve time series prediction, which utilized more parameters than the classic type-2 fuzzy model. Furthermore, the decoupling extended Kalman filter (DEKF) was also used to optimize the parameters of IT2IFLS-TSK [38]. By introducing non-membership functions and intuitionistic fuzzy indexes, the functionality of fuzzy logic systems can be improved.…”

Section: Introductionmentioning

confidence: 99%

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Wang

Luo

2019

IEEE Access

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Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results. Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system. In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN). By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree. In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge. Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data. The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data. By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme. INDEX TERMS Fuzzy neural network, intuitionistic fuzzy sets, interval type-2 fuzzy systems, metacognition, sequential learning.

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Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system

Cited by 10 publications

References 26 publications

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Interval Type-2 Intuitionistic Fuzzy Logic Systems - A Comparative Evaluation

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

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