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A Novel Fuzzy Time Series Forecasting Model Based on the hybrid wolf pack algorithm and Ordered Weighted Averaging Aggregation Operator

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第6期22卷 1832-1850页

作者： Xian, Sidong Li, Tangjin Cheng, Yue Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China

The fuzzy time series has received extensive attention since it was proposed and it has been widely used in various practical applications. This study proposes a new fuzzy time series forecasting model which considers a hybrid wolf pack algorithm (HWPA) and an ordered weighted averaging (OWA) aggregation operator for fuzzy time series. The HWPA is adopted to obtain a suitable partition of the universe of discourse to promote the forecasting performance. Furthermore, the improved OWA aggregation method is applied to make the aggregation of historical information more practical. To overcome the deficiency of slow convergence speed and easy to entrap into the local extremum of the wolf pack algorithm (WPA), the chemotactic behavior and elimination-dispersal behavior of bacterial foraging optimization (BFO) are employed to optimize the scouting behavior of WPA. The actual enrollments data of the University of Alabama and Taiwan Futures Exchange (TAIFEX) are utilized as the benchmark data and the computational results of both training and testing phases all indicate that the new forecasting model outperforms other existing models. The robustness of the proposed model is also tested and the robust results can be obtained when the historical data are inaccurate.

关键词： Fuzzy time series wolf pack algorithm Bacterial foraging optimization Ordered weighted averaging aggregation operator hybrid wolf pack algorithm TAIFEX

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Research on reliability of sports intelligent training system based on hybrid wolf pack algorithm and IoT

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SOFT COMPUTING 2023年第14期27卷 10189-10197页

作者： Chen, Wenfeng Huang, Xinyan Guangdong Polytech Environm Protect Engn Dept Sport Foshan 528216 Guangdong Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China

To expand the application scope of the WCA algorithm in the actual process, this research has optimized the problems in the algorithm in two aspects: The first aspect further improves the operating mechanism of the WCA algorithm and improves the performance of optimization problem. In the second aspect, other optimization strategy mechanisms of the WCA algorithm are introduced to enable the algorithm to optimize multi-objective and multidimensional problems. This paper studies a physical test system and sports intelligent based on hybrid wolf pack algorithm and IoT. The system collects and sends data from the data collection terminal of the Web server, receives the data through the wireless module, and sends it to the Web server through the network. The Web server processes and stores the data information to generate a database, and users can view their own sports information by logging in to the Web service program with the account password. In addition, the teacher and administrator accounts have the ability to view all users' exercise information. The system adds three sports items, pull-ups, squats, and standing long jumps. At the same time, the system uses a general motion recognition algorithm, which can effectively reuse and add new sports items. According to the actual needs of intelligent sports training, this paper combines somatosensory technology, bone tracking technology, and motion recognition algorithm to realize a high-precision, low-latency intelligent sports training system.

关键词： hybrid wolf pack algorithm Internet of Things Intelligent training Reliability research

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