High-dimensional data regression presents significant challenges due to factors such as strong nonlinearity among features, an excessive number of intermediate variables, and rule explosion, all of which hinder the model's ability to capture complex features and achieve low regression accuracy. This paper proposes a method for high-dimensional data regression using a Hierarchical Fuzzy Topological System (HFTS). The HFTS adopts a modular design, where each layer consists of an independent fuzzy logic system, enabling flexible operation based on feature distribution and output requirements. It utilizes a Graph Neural Network (GNN)-based hierarchical feature classification approach to group high-dimensional data, mapping features into nodes and establishing edges based on similarity. This process creates a topological structure that facilitates high-density feature representation through neighborhood aggregation. HFTS introduces a cross-layer rule-sharing mechanism and an interpolation expansion algorithm to smooth fuzzy rules, thereby reducing interaction complexity. Additionally, an adaptive weight adjustment strategy dynamically optimizes feature importance, enhancing both robustness and predictive accuracy. When applied to eleven KEEL regression datasets, HFTS demonstrates superior accuracy, effectively addressing high-dimensional interactions while maintaining a balance between interpretability and performance

This article considers a multiple leaks' localization method in fluid pipelines based on the change in the sound velocity of an ultrasonic signal. First, the sound velocity of an ultrasonic signal is denoised by wavelet packet analysis (WPA). Second, the mathematical relationship between the position of a leak and the change in the sound velocity of an ultrasonic signal at the ends of a pipeline is established based on the motion equation and the continuity equation in the transient flow equation of a pipeline. As a consequence, the localization of the leakage position of the proposed method depends only on the change in the sound velocity of the ultrasonic signal at the ends of the pipeline rather than the time difference in a negative pressure wave arriving at the ends of the pipeline. Finally, the change in the sound velocity of the ultrasonic signal is used and it is proven to be efficient to locate the actual leak number. The simulation results show that the proposed method can locate multiple leak positions in a pipeline.

A robust fuzzy predictive control (RFPC) based on Takagi-Sugeno fuzzy model is proposed for systems with uncertainties, time-varying delays, unknown disturbances as well as strong nonlinearity. Firstly, T-S fuzzy model is built by a number of linear sub-models and nonlinear membership functions. Then, by introducing the output tracking error to this fuzzy model, the novel augmented state space model is presented to independently regulate the process state variables and output tracking error. The control law of proposed RFPC is further designed based on this extended model, which can guarantee the process state to be fast convergent and make the process output track the set-point well. Moreover, it increases the ability of adjustment for the proposed controller. Utilizing Lyapunov-Krasovskii method, optimized control theory, and H-infinity control method, the stable sufficient conditions are given for the designed control law to make sure the asymptotical stability of the nonlinear uncertain system with time-varying delay and unknown disturbances. The gains of the controller can be obtained by solving these stabilized conditions in form of linear matrix inequality constraints. At last, a case study of continuous stirred tank reactor manifests that the proposed RFPC method can bear the larger range of time delay, overcome the uncertainties and unknown disturbances well and have better tracking performance.

A delay‐range‐dependent robust constrained model predictive control is proposed for discrete‐time system with uncertainties and unknown disturbances. The dynamic characteristic of the discrete‐time system is established as a new extended state space model in which state variables and output tracking error are integrated and regulated independently. It is used as the design of control law of system, which cannot only guarantee the convergence and tracking performance but also offer more degrees of freedom for designed controller. Unlike the traditional robust model predictive control (RMPC), the novel, less conservative, and more simplified delay‐range‐dependent stable conditions are derived by linear matrix inequality (LMI) theory and some relaxed technologies, which make use of the information of the upper and lower bounds of the time‐varying delay. Meanwhile, the H_∞ performance index is introduced in the RMPC controller design, which can reject any unknown bounded disturbances. As a result, the design controller has better abilities of both tracking and disturbance rejection. The control results on the liquid level of tank system show that the proposed control method is effective and feasible.