Research on Spectrum Sensing System Based on Composite Neural Network

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

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Improved Cooperative Spectrum Sensing Algorithm with Artificial Neural Network

Advances in Intelligent Systems and Computing - Complex, Intelligent, and Software Intensive Systems ◽

10.1007/978-3-319-93659-8_58 ◽

2018 ◽

pp. 639-649

Author(s):

Jingcheng Miao ◽

Xiaoou Song ◽

Zili Wang ◽

Xu-an Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Spectrum Sensing ◽

Cooperative Spectrum Sensing ◽

Artificial Neural

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A Sensing System for an Autonomous Mobile Robot Based on the Paraconsistent Artificial Neural Network

Knowledge-Based and Intelligent Information and Engineering Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-642-15393-8_18 ◽

2010 ◽

pp. 154-163 ◽

Cited By ~ 1

Author(s):

Claudio Rodrigo Torres ◽

Jair Minoro Abe ◽

Germano Lambert-Torres ◽

João Inácio Da Silva Filho ◽

Helga Gonzaga Martins

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mobile Robot ◽

Autonomous Mobile Robot ◽

Sensing System ◽

Artificial Neural

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Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

10.21203/rs.3.rs-22343/v1 ◽

2020 ◽

Author(s):

Rahil Sarikhani ◽

Farshid Keynia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Cognitive Radio ◽

Spectrum Sensing ◽

Recurrent Neural Network ◽

Cooperative Spectrum Sensing ◽

Primary User ◽

Secondary Users ◽

Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

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