A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs

Abstract

To improve the intelligence and accuracy of the Situation Assessment (SA) in complex scenes, this work develops an improved fuzzy deep neural network approach to the situation assessment for multiple Unmanned Aerial Vehicle(UAV)s. Firstly, this work normalizes the scene data based on time series and use the normalized data as the input for an improved fuzzy deep neural network. Secondly, adaptive momentum and Elastic SGD (Elastic Stochastic Gradient Descent) are introduced into the training process of the neural network, to improve the learning performance. Lastly, in the real-time situation assessment task for multiple UAVs, conventional methods often bring inaccurate results for the situation assessment because these methods don’t consider the fuzziness of task situations. This work uses an improved fuzzy deep neural network to calculate the results of situation assessment and normalizes these results. Then, the degree of trust of the current result, relative to each situation label, is calculated with the normalized results using fuzzy logic. Simulation results show that the proposed method outperforms competitors.

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey

“…In addition, the authors of (Deng et al 2017 ) presented an FDNN for classification tasks, such as natural scene image categorization and stock trend prediction. Similar studies for image classification can be found in (Guan et al 2020 ; Kunchala et al 2020 ; Liu et al 2020a , b ; Liu et al 2020a , b ; Manchanda et al 2020 ; Tianyu and Xu 2020 ; Yeganejou and Dick 2018 , 2019 ; Yeganejou et al 2020 ; Zhang et al 2020a , b , c ).…”

“…The study presented a hybrid diagnosis strategy (HDS) using fuzzy inference and a DNN to detect COVID-19 patients. Moreover, the novel hybrid approach to DNFS has been implemented for tumor and cancer detection and segmentation (Banerjee et al 2020 ; Lima et al 2020 ; Mudiyanselage et al 2020 ; Özyurt et al 2019 ; Pitchai et al 2020 ; Rahouma et al 2019 ; Sengan et al 2020 ; Shen et al 2020 ; Yang et al 2020 ; Zhang et al 2020a , b , c ).…”

“…The existing approaches are time-consuming, unreliable, and incompetent in coping with uncertainty. Therefore, an FDNN was implemented in (Gobinath and Madheswaran 2020 ; Lin et al 2020 ; Yun et al 2020 ; Zhang et al 2020a , b , c ; Zheng et al 2017 ) to diagnose the faults in machines, provide a forecast, and alert managers for possible industrial accidents in advance. In addition, the study of (Remya and Sasikala 2019 ) used hybridization of the back-propagation in deep learning and fuzzy logic decision tree for rubberized coir fiber classification.…”

“…To cope with these problems, few studies from the literature (Aviles et al 2016 ; El Hatri and Boumhidi 2018 ; Zhang et al 2020a , b , c ) have combined a DNN with fuzzy systems to produce a novel deep neuro-fuzzy system (DNFS) . Fuzzy systems are structures established on fuzzy techniques oriented toward information processing, and are mainly used for implementation in systems where the use of classical binary logic is impossible or difficult.…”

Navigation Situation Assessment of Autonomous Surface Vehicles in a Cooperative Hunting Environment

“…By combining the analytic hierarchy process and fuzzy assessment, a fuzzy comprehensive assessment model was constructed for describing the degree of safety and integrating the effective data [24]. In [25], an improved fuzzy neural network-based situation assessment method was proposed for multiple vehicles, improving the intelligence and accuracy of assessments in a complex environment. However, the situation assessment research for cooperative hunting of multiple ASVs is still open for study.…”