Stability analysis of delayed Takagi-Sugeno fuzzy systems: a new integral inequality approach

An, Jiyao; Liu, Xinzhi; Wen, Guanghui

doi:10.22436/jnsa.010.04.53

Cited by 11 publications

(2 citation statements)

References 31 publications

(107 reference statements)

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“…With the rapid growing of wireless communications technology, combination of multiple localization technologies system which can compensate and overcome the weaknesses in single location technology has become a popular proposal [21]. Fuzzy logic control is an adequate methodology for designing controllers to certain nonlinear systems [23] and has found numerous applications in a wide range of real engineering problems [24,25]. To overcome their individual limitations, by combining with GPS, WiFi, and CN positioning technology, a novel FLM is firstly proposed to enhance their individual positioning features.…”

Section: Our Contributionsmentioning

confidence: 99%

Fuzzy-Based Hybrid Location Algorithm for Vehicle Position in VANETs via Fuzzy Kalman Filtering Approach

Tang

et al. 2019

Advances in Fuzzy Systems

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Location information is very critical to VANETs such as navigation, routing, network management, and road congestion. In this paper, the vehicle location problem under urban road conditions is investigated by employing the GPS, WiFi, and Cellular Network (CN) positioning systems and by developing neighbor vehicle utilization in VANETs. Since GPS is possibly affected by satellite signal in real urban environment, while WiFi is only suitable for urban and CN is affected by the number of Base Stations (BSs) and signal strength, then a fuzzy-based hybrid location algorithm is developed. The algorithm has some advantages that it can enhance these positioning features by establishing a new fuzzy-weighting location mechanism (FLM) and also can adjust dynamically the measurement noise covariance by making use of a novel fuzzy Kalman filtering method. Finally, experiment results are given to show effectiveness and merit of the proposed approach.

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Section: Our Contributionsmentioning

confidence: 99%

Fuzzy-Based Hybrid Location Algorithm for Vehicle Position in VANETs via Fuzzy Kalman Filtering Approach

Tang

et al. 2019

Advances in Fuzzy Systems

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“…However, the emergence of fuzzy theory in the 19th century provided a good research direction for the processing and unification of data uncertainty. Moreover, fuzzy systems based on fuzzy theory have been continuously researched, which are widely used in many practical problems such as control engineering, pattern recognition, and artificial intelligence [6]. Compared with the previous deterministic description, fuzzy theory can describe the original data more naturally, furthermore, it has better advantages in dealing with the limitations of data and the robustness of noise [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information

et al. 2019

IEEE Access

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As a key part of the method of improving traffic capacity, traffic flow prediction is becoming a research hot-spot of traffic science and intelligent technology, in which the accuracy of traffic flow prediction is particularly concerned. In this paper, a novel fuzzy-based convolutional neural network (F-CNN) method is proposed to predict the traffic flow more accurately, in which a fuzzy approach has been applied to represent the traffic accident features when introducing uncertain traffic accidents information into the CNN at the first time. First, for the sake of extracting the spatial-temporal characteristics of the traffic flow data, this paper divides the whole area into small blocks of 32 × 32 and constructs three trend sequences with inflow and outflow types. Second, uncertain traffic accident information is generated from the real traffic flow data by utilizing a fuzzy inference mechanism. Finally, the F-CNN model is realized to train the internal information of the trend sequence, the uncertain traffic accident information, and the external information. Moreover, pre-training and fine-tuning strategies are efficiently developed to learn the parameters of the F-CNN. At last, the real Beijing taxicab trajectory and the meteorology datasets are employed to show that the proposed method has superior performance compared with the state-of-the-art approaches.INDEX TERMS Traffic flow prediction, traffic accident information, fuzzy inference system (FIS), convolutional neural network (CNN), fuzzy-based convolutional neural network (F-CNN).

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A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features

Chen

et al. 2018

Future Generation Computer Systems

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Stability analysis of delayed Takagi-Sugeno fuzzy systems: a new integral inequality approach

Cited by 11 publications

References 31 publications

Fuzzy-Based Hybrid Location Algorithm for Vehicle Position in VANETs via Fuzzy Kalman Filtering Approach

Fuzzy-Based Hybrid Location Algorithm for Vehicle Position in VANETs via Fuzzy Kalman Filtering Approach

A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information

A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features

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