An OFDM Channel Estimation Method with Radial Basis Function Neural Network

For OFDM system, we proposed a channel estimation method based on radial basis function neural network (RBFNN). The neural network with Gaussian basis function is established according to the pilot pattern, where the network parameters are obtained by training channel response of pilot subcarriers as objective values for input samples. With the established network, channel coefficients of non-pilot subcarriers can be predicted. The simulation results indicate that the proposed algorithm performs well in OFDM systems under Rayleigh multipath fading channel.

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Channel estimation for OFDM systems based on adaptive radial basis function networks

IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004 ◽

10.1109/vetecf.2004.1400079 ◽

2005 ◽

Cited By ~ 3

Author(s):

Tao Cui ◽

C. Tellambura

Keyword(s):

Channel Estimation ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Networks ◽

Ofdm Systems ◽

Radial Basis

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Channel estimation for ofdm systems using adaptive radial basis function networks

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2002.800619 ◽

2003 ◽

Vol 52 (1) ◽

pp. 48-59 ◽

Cited By ~ 29

Author(s):

Xiaobo Zhou ◽

Xiaodong Wang

Keyword(s):

Channel Estimation ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Networks ◽

Ofdm Systems ◽

Radial Basis

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Radial Basis Function Neural Networks for Channel Estimation in MIMO-OFDM Systems

Arabian Journal for Science and Engineering ◽

10.1007/s13369-013-0586-1 ◽

2013 ◽

Vol 38 (8) ◽

pp. 2173-2178 ◽

Cited By ~ 5

Author(s):

M. Nuri Seyman ◽

Necmi Taspinar

Keyword(s):

Neural Networks ◽

Channel Estimation ◽

Radial Basis Function ◽

Basis Function ◽

Ofdm Systems ◽

Radial Basis ◽

Mimo Ofdm

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Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network

Journal of Robotics ◽

10.1155/2019/4141269 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Zhiguang Liu ◽

Jianhong Hao

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Real Time ◽

Basis Function ◽

Control Method ◽

Contact Point ◽

Estimation Method ◽

Human Robot Interaction ◽

Data Matching ◽

Radial Basis

To solve synchronization movement problem in human-robot haptic collaboration, the robot is often required to recognize intention of the cooperator. In this paper, a method based on radial basis function neural network (RBFNN) model is presented to identify the motion intention of collaborator. Here, the human intention is defined as the desired velocity in human limb model, of which the estimation is obtained in real time based on interaction force and the contact point movement characteristics (current position and velocity of the robot) by the trained RBFNN model. To obtain training samples, adaptive impedance control method is used to control the robot during the data acquisition process, and then the data matching is executed due to the phase delay of the impedance function. The advantage of proposed intention estimation method according to the system real-time status is that the model overcomes the shortcoming of difficult estimating the human body impedance parameters. The experimental results show that this proposed method improves the synchronization of human-robot collaboration and reduces the force of the collaborator.

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