A5002771983- Time Series Analysis and Forecasting
- Cognitive Science and Mapping
- Cognitive Computing and Networks
- Stock Market Forecasting Methods
- Neural Networks and Applications
- Rough Sets and Fuzzy Logic
- Complex Systems and Time Series Analysis
- Multi-Criteria Decision Making
- Fuzzy Systems and Optimization
- Advanced Algorithms and Applications
- Chaos control and synchronization
- Electric Power System Optimization
- Advanced Decision-Making Techniques
- Data Management and Algorithms
- Optimization and Variational Analysis
- Advanced Graph Neural Networks
- Optimal Power Flow Distribution
- Advanced Computational Techniques and Applications
- Evaluation and Optimization Models
- Forecasting Techniques and Applications
- Optimization and Mathematical Programming
- Recommender Systems and Techniques
- Microgrid Control and Optimization
- Caching and Content Delivery
- Grey System Theory Applications
Beijing Normal University
2023-2024
Chongqing University of Posts and Telecommunications
2021
Hubei University of Technology
2019
In this paper, the mixed integer linear programming (MILP) for solving unit commitment (UC) problems in a hybrid power system containing thermal, hydro, and wind have been studied. To promote its efficiency, an improved MILP approach has proposed, while symmetric problem formulas solved by reforming hierarchical constraints. Experiments on different scales conducted to demonstrate effectiveness of proposed approach. The results indicate dramatic efficiency promotion compared other popular...
Fuzzy cognitive maps (FCMs) are directed graphs with multiple nodes, making them well-suited for addressing multivariate time series (MTS) forecasting problems. When MTS, it is crucial to treat each vector of the MTS as a whole, considering both causalities between different variables at timepoint (spatial relationship) and historical vectors future (temporal relationship). Existing FCM-based models often fail whole do not distinctly reflect temporal relationship spatial in MTS. To address...
Along with the abundant appearance of interval-valued time series (ITS), study on ITS clustering, especially shape-based is becoming increasingly important. As an effective approach to extracting trend information in series, fuzzy trend-granulation addresses needs clustering. However, when ITS, unequal-size granules are inevitably produced, which makes clustering difficult and challenging. Facing this issue, paper aims generalize widely used Fuzzy C-Means (FCM) algorithm a based FCM for To...
This paper presents a time series classification method which utilizes ensemble learning. The multi-class is reduced to several binary tasks. Base classifiers incorporate fuzzy cognitive map. Each of them learns independently correctly associate with specific categories. Predicted memberships are later aggregated into the final class assignment. We compare four different decomposition methods implementing "one-vs-one" and "one-vs-all" approaches. test these on real-world datasets...
Being an effective tool in conducting time series forecasting, fuzzy cognitive maps (FCMs) characterize the causalities of past values on future by real-valued weights [-1,1]. But for interval-valued (ITS), intervals are affected various uncertain factors and thus involve uncertainty. At this point, no longer enough characterizing such causalities. FCMs with i.e. interval (ICMs) become necessary. In case, how to determine ICM remains a challenging problem. Aiming at problem help proposed...
Predicting interval-valued time series (ITS) has long been a subject that attracted researchers from diverse range of fields including economics and finance. Since the data relations in real world are usually highly sophisticated inaccurate, modeling complex systems is challenging task especially for large scale, inaccurate non stationary datasets. Fuzzy Cognitive Map (FCM) become powerful tool analyzing systems, widely used various fields. As increasing use FCM, there still very little...