This paper addresses the fuzzy adaptive finite-time control problem for nonlinear systems with unknown nonlinearities and external disturbances. Fuzzy logic systems are employed to approximate the unknown nonlinear functions within the control framework. A dynamic surface control approach, integrated with first-order filters, is adopted to avoid the “complexity explosion” issue inherent in conventional backstepping design. By incorporating adaptive backstepping and finite-time stability theory, an adaptive fuzzy control scheme is developed, ensuring that all closed-loop signals remain bounded within finite time despite uncertainties and disturbances. Simulation results demonstrate the effectiveness and feasibility of the proposed method.
Short-Term load forecasting (STLF) is essential for the secure operation and effective planning of power systems. Accurate load forecasting not only ensures system reliability but also supports economic dispatch, optimal resource allocation, and risk management. However, due to the highly nonlinear and volatile nature of load patterns, as well as the influence of various external factors such as weather conditions, holidays, and human activities, single-step forecasting is often insufficient to meet practical requirements, making multi-step STLF particularly important for operational decision-making and planning. To address this challenge, this paper proposes a multi-population genetic programming (MPGP) approach to multi-step STLF. This approach evolves prediction models through multiple independent populations, with each population specifically trained and optimized for a fixed forecasting time step, thereby generating accurate predictions corresponding to each time step. Experimental results on real-world datasets demonstrate that the proposed approach can effectively capture complex load variation patterns, adapt to nonlinear dynamics, and maintain high accuracy across multiple steps, confirming the feasibility, robustness, and reliability of MPGP for multi-step STLF in power systems.
Dynamic characteristics are inherent in industrial processes. With increasing system complexity and the growing number of sensors, data-driven modeling faces challenges in terms of computational burden, and incipient faults become harder to detect promptly. To address these issues, this study proposes a vector autoregressive-based dynamic process modeling method that decomposes the monitoring space into dynamic and static components. An efficient sparse dynamic matrix estimation algorithm is further developed for offline model optimization, and a dissimilarity analysis-based approach is introduced for incipient-fault detection in the static component. Experiments on the Tennessee Eastman process benchmark model validate the effectiveness of the proposed method.
Relational database querying in Tibetan remains a markedly low-resourced endeavor. Existing Text-to-SQL models have only been evaluated on English or Chinese corpora, leaving Tibetan entirely unexplored. To address these issues, TSpider as the first Tibetan Text-to-SQL dataset is constructed by translating the CSpider corpus and comprising 11,840 Tibetan natural-language queries. Furthermore, the Input Feature Enhancement (IFE)-LLM framework is employed: its cross-encoder is adapted to Tibetan by integrating CINO, and the downstream open-source large language model is then prompted to generate executable SQLite SQL statements. Experimental evaluation on TSpider reveals that our framework attains 31.8
Drug target identification is a constraint multi-objective optimization problem with NP-hard characteristics, which aims to select a set of nodes from a gene network to control the transition between disease and normal state. However, existing related studies mainly focus on the evolutionary strategies, ignoring the relationship between nodes and constraint optimization. Therefore, this paper proposes a novel node importance method by analyzing the relationship between adjacent nodes with different connectivity differences. Then, the node importance is utilized to design a population initialization strategy to select better nodes. In experiments, this strategy is embedded into three algorithms to verify its effectiveness. Experimental results on three cancer genomic datasets show that the algorithm incorporating node importance knowledge achieves better performance compared to the original algorithm regarding two multiobjective optimization indicators and one biological significance indicator.
