As a pivotal role in the control, optimization, and monitoring of contemporary industrial processes, soft sensors are frequently employed in the prediction of key quality variables. To achieve accurate prediction of key quality variables in industrial processes, a soft sensor modeling method based on the self-organizing fuzzy neural network with the clustering, merging, and splitting scheme (SOFNN-CMS) is proposed. First, the supervised fuzzy C-means clustering algorithm is proposed to identify the appropriate initial center and width of the fuzzy neural network, obtaining appropriate initial fuzzy rules. Then, a neuron merging and splitting strategy is designed to adjust the structure of the fuzzy neural network, by merging and splitting the hidden neurons according to the distance of clusters, increasing the adaptability of the fuzzy neural network. Besides, to accelerate the convergence of estimation errors, an improved Levenberg Marquardt algorithm is utilized to update neural network parameters in the training phase, realizing the soft sensor modeling of key quality variables. The effectiveness of the proposed SOFNN-CMS neural network is demonstrated on two benchmark problems and an industrial debutanizer column. Finally, the experiments showcase that the proposed SOFNN-CMS neural network can obtain better soft sensor modeling performance with a compact structure.

This paper introduces logistic regression with sparse and smooth regularizations (LR-SS), a novel framework that simultaneously enhances both classification and feature extraction capabilities of standard logistic regression. By incorporating a family of symmetric smoothness constraints into sparse logistic regression, LR-SS uniquely preserves underlying structures inherent in structured data, distinguishing it from existing approaches. Within the minorization–maximization (MM) framework, we develop an efficient optimization algorithm that combines coordinate descent with soft-thresholding techniques. Through extensive experiments on both simulated and real-world datasets, including time series and image data, we demonstrate that LR-SS significantly outperforms conventional sparse logistic regression in classification tasks while providing more interpretable feature extraction. The results highlight LR-SS’s ability to leverage sparse and symmetric smooth regularizations for capturing intrinsic data structures, making it particularly valuable for machine learning applications requiring both predictive accuracy and model interpretability.

This paper introduces logistic regression with sparse and smooth regularizations (LR-SS), a novel framework that enhances classification and feature extraction by incorporating both sparsity and smoothness constraints. We propose a family of smooth regularizations designed to capture temporal and spatial structural patterns inherent in data. The optimization problem is solved efficiently using the minorization-maximization (MM) framework combined with coordinate descent and soft-thresholding techniques. Through extensive experiments on both simulated and real-world datasets, including time series and image data, we demonstrate that LR-SS generally outperforms conventional sparse logistic regression methods in terms of classification accuracy and feature extraction quality. Therefore, LR-SS provides a powerful tool for machine learning applications requiring both accurate prediction and strong interpretability.

Infrared and visible image fusion aims to integrate useful information from the source image to obtain a fused image that not only has excellent visual perception but also promotes the performance of the subsequent object detection task. However, due to the asymmetry between image fusion and object detection tasks, obtaining superior visual effects while facilitating object detection tasks remains challenging in real-world applications. Addressing this issue, we propose a novel visual attention fusion network for infrared and visible image fusion (VAFNet), which can bridge bottom-up and top-down features to achieve high-quality visual perception while improving the performance of object detection tasks. The core idea is that bottom-up visual attention is utilized to extract multi-layer bottom-up features for ensuring superior visual perception, while top-down visual attention determines object attention signals related to object detection tasks. Then, a bidirectional attention integration mechanism is designed to naturally integrate two forms of attention into the fused image. Experiments on public and collection datasets demonstrate that VAFNet not only outperforms seven state-of-the-art (SOTA) fusion methods in qualitative and quantitative evaluation but also has advantages in facilitating object detection tasks.