To address the contradiction between the convergence error and convergence rate in the LMS algorithm, this study proposes a variable-step-size adaptive filter algorithm with a momentum term based on the logistic function. First, the normalization LMS algorithm is obtained by seeking the extremum under the Lagrange function constraint. Second, to reduce the convergence error of the algorithm, the logistic model is used as a function model of step size variation with error, resulting in a variable-step normalization LMS algorithm. Our experimental results demonstrate that this algorithm achieves smaller convergence errors compared to those of the traditional LMS algorithm. Finally, to further improve the convergence rate of the algorithm, a momentum term is introduced into the weight coefficient update process of the LMS algorithm. This leads to the development of a variable-step adaptive filter algorithm with a momentum term based on the logistic function. The impact of different parameters on the algorithm performance is also investigated. In order to verify the rationality of the proposed algorithm, a dynamic system mathematical model was identified using the proposed algorithm. The results showed that the proposed algorithm had an identification accuracy of over 97% for the mathematical model parameters and a suppression of over 99% for noise. In order to verify the engineering application value of the proposed algorithm, real-time vibration data fitting experiments were conducted in the Aeroelasticity Laboratory of the China Aerodynamics Research and Development Center, and their results were compared with three algorithms: ARMAX, N4SID, and LMS. The results showed that the proposed algorithm had a higher fitting accuracy than the three others. Through simulations and experiments, it is demonstrated that this study has value both theoretically and in engineering applications, promoting engineering applications of adaptive filtering algorithms and providing strong support for the research of adaptive control.

Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed. The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river. The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features. This work analyzes 17 new input features, namely conductivity (COND), salinity (SAL), turbidity (TUR), dissolved solids (DS), nitrate (NO3), chloride (Cl), phosphate (PO4), arsenic (As), chromium (Cr), zinc (Zn), calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), E. coli, and total coliform, in predicting WQI using machine learning techniques. Five regression algorithms—random forest (RF), AdaBoost, support vector regression (SVR), decision tree regression (DTR), and multilayer perception (MLP)—are applied for preliminary model selection. The results show that the RF algorithm exhibits better prediction performance, with R2 of 0.974. Then, this work proposes a modified RF by incorporating the synthetic minority oversampling technique (SMOTE) into the conventional RF method. The proposed modified RF method is shown to achieve 77.68%, 74%, 69%, and 71% accuracy, precision, recall, and F1-score, respectively. In addition, the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction. The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation.

The static aeroelastic behaviours of a flat‐plate forward‐swept wing model in the vicinity of static divergence are investigated by numerical simulations and wind tunnel tests. A medium fidelity model based on the vortex lattice method (VLM) and nonlinear structural analysis is proposed to calculate the displacements of the wing structure with large deformation. Follower forces effect and geometric nonlinearity are considered to calculate the deformation of the wing by finite element method (FEM). In the wind tunnel tests, the divergence dynamic pressure is predicted by the Southwell method, and the static aeroelastic displacement is measured by a photogrammetric method. The results obtained by the medium fidelity model calculations show reasonable agreement with wind tunnel test results. A high fidelity model based on coupled computational fluid dynamics (CFD) and computational structural dynamics (CSD) predicts better results of the wing tip displacement when the freestream dynamic pressure is approaching the divergence dynamic pressure.