This article investigates the problem of fault estimation in nonlinear uncertain manipulator systems with time-delay. A novel fault estimation scheme is proposed, which optimizes the iterative learning (IL) estimator performance using a neural network (NN)-based reinforcement learning (RL) approach. Specifically, first, with the purpose of enhancing robustness and adaptability of RL, a new adaptive exponential reward function is designed. Then, to improve the performance of fault estimation, speed and accuracy are designed as optimization objectives. Simultaneously, by leveraging the IL estimator, the NN is continuously optimized in each iteration process to mitigate the issues of gradient vanishing and explosion. Further, by incorporating the H$\mathrm{\infty }$ performance index into the observer, an asymptotically convergent estimated error can be attained. Finally, numerical simulations are conducted to demonstrate the effectiveness of our method.

Purpose: High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.Design/methodology/approach: There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.Findings: In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.Originality/value: The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.