Improved Glasius Bio-inspired Neural Network for Target Search by Multi-agents

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

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Simulation of football sport PID controller based on BP neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189570 ◽

2020 ◽

pp. 1-13

Author(s):

L.V. Qiangguo

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Bp Neural Network ◽

Pid Controller ◽

Single Agent ◽

Control Effect ◽

Learning Problem ◽

Learning Space ◽

Multi Agent ◽

Multi Agents

Multi-agent reinforcement learning in football simulation can be extended by single-agent reinforcement learning. However, compared with single agents, the learning space of multi-agents will increase dramatically with the increase in the number of agents, so the learning difficulty will also increase. Based on BP neural network as the model structure foundation, this research combines PID controller to control the process of model operation. In order to improve the calculation accuracy to improve the control effect, the prediction output is obtained through the prediction model instead of the actual measured value. In addition, with the football robot as the object, this research studies the multi-agent reinforcement learning problem and its application in the football robot. The content includes single-agent reinforcement learning, multi-agent system reinforcement learning, and ball hunting, role assignment, and action selection in football robot decision strategies based on this. The simulation results show that the method proposed in this paper has certain effects.

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Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11171965 ◽

2019 ◽

Vol 11 (17) ◽

pp. 1965 ◽

Cited By ~ 3

Author(s):

Yanan You ◽

Zezhong Li ◽

Bohao Ran ◽

Jingyi Cao ◽

Sudi Lv ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Processing System ◽

Deep Convolutional Neural Network ◽

Detection Methods ◽

Optical Remote Sensing ◽

Broad Area ◽

Target Search ◽

Ship Detection

High-resolution optical remote sensing data can be utilized to investigate the human behavior and the activities of artificial targets, for example ship detection on the sea. Recently, the deep convolutional neural network (DCNN) in the field of deep learning is widely used in image processing, especially in target detection tasks. Therefore, a complete processing system called the broad area target search (BATS) is proposed based on DCNN in this paper, which contains data import, processing and storage steps. In this system, aiming at the problem of onshore false alarms, a method named as Mask-Faster R-CNN is proposed to differentiate the target and non-target areas by introducing a semantic segmentation sub network into the Faster R-CNN. In addition, we propose a DCNN framework named as Saliency-Faster R-CNN to deal with the problem of multi-scale ships detection, which solves the problem of missing detection caused by the inconsistency between large-scale targets and training samples. Based on these DCNN-based methods, the BATS system is tested to verify that our system can integrate different ship detection methods to effectively solve the problems that existed in the ship detection task. Furthermore, our system provides an interface for users, as a data-driven learning, to optimize the DCNN-based methods.

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