Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Lin, Cheng‐Jian; Lin, Chu‐Hsing; Jhang, Jyun-Yu

doi:10.3390/electronics9040631

Cited by 1 publication

(2 citation statements)

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“…Recently, fuzzy neural networks (FNNs) [35][36][37][38][39] that have a human-like fuzzy inference mechanism and the powerful learning functions of neural networks have been widely used in various fields, such as classification, control, and forecasting. Asim et al [35] applied an adaptive networkbased fuzzy inference system to classification problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Lin et al [36] used an interval type-2 FNN and tool chips to predict flank wear, and their method yielded superior prediction results. A few researchers have used a locally recurrent functional link fuzzy neural network [37] and Takagi-Sugeno-Kang-type FNNs [38][39] to solve system identification and prediction problems, and both methods have yielded good results. In this study, an FNN was embedded into a deep learning network to reduce the number of parameters used in the network and obtain superior classification results.…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

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