In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method.

This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.

Unmanned ground vehicle control in unknown environments is hindered by terrain variation, obstacle complexity, and sensor noise. This study proposes a light-detection-and-ranging-based control system that integrates fuzzy terrain traversability assessment (FTTA) and adaptive artificial potential field (AAPF) algorithms for terrain detection and obstacle avoidance. Cloth simulation filtering (CSF) is used to preprocess point clouds for distinguishing between terrain and aboveground points. The FTTA algorithm then identifies terrain obstacles using slope and height features, with aboveground points classified as obstacles by height. Moreover, the AAPF algorithm integrates both terrain and aboveground points to adaptively adjust the exclusion radius and improve path planning. Experimental analysis revealed that FTTA with CSF achieved a ground point retention rate of 0.682, a height variance of 0.087, and a processing time of 0.016 s. These values maintained the slope estimation angles close to the true values and ensured stable and rapid computation. In navigation experiments, the AAPF algorithm achieved a reduced travel distance of 62.4 ± 3.2 m and a reduced travel time of 154 ± 5 s. Compared with the baseline APF method, this represents a reduction of 21% in navigation time, indicating improved efficiency and stability.