Complex-Chebyshev Functional Link Neural Network Behavioral Model for Broadband Wireless Power Amplifiers

2012 ◽  
Vol 60 (6) ◽  
pp. 1979-1989 ◽  
Author(s):  
Mingyu Li ◽  
Jinting Liu ◽  
Yang Jiang ◽  
Wenjiang Feng
Keyword(s):  
Neural Network ◽  
Power Amplifiers ◽  
Behavioral Model ◽  
Wireless Power ◽  
Functional Link ◽  
Broadband Wireless ◽  
Chebyshev Functional ◽  
Functional Link Neural Network

Attention-Based Deep Neural Network Behavioral Model for Wideband Wireless Power Amplifiers

2020 ◽  
Vol 30 (1) ◽  
pp. 82-85 ◽  
Author(s):  
Zhijun Liu ◽  
Xin Hu ◽  
Ting Liu ◽  
Xiuhua Li ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Amplifiers ◽  
Deep Neural Network ◽  
Behavioral Model ◽  
Wireless Power

A Theoretical and Empirical Study of Functional Link Neural Networks (FLANNs) for Classification

2010 ◽  
pp. 545-573 ◽  
Author(s):  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Empirical Study ◽  
Particle Swarm ◽  
Functional Link ◽  
Swarm Optimization ◽  
Chebyshev Functional ◽  
Functional Link Neural Networks ◽  
Functional Link Neural Network

In this chapter, the primary focus is on theoretical and empirical study of functional link neural networks (FLNNs) for classification. We present a hybrid Chebyshev functional link neural network (cFLNN) without hidden layer with evolvable particle swarm optimization (ePSO) for classification. The resulted classifier is then used for assigning proper class label to an unknown sample. The hybrid cFLNN is a type of feed-forward neural networks, which have the ability to transform the non-linear input space into higher dimensional space where linear separability is possible. In particular, the proposed hybrid cFLNN combines the best attribute of evolvable particle swarm optimization (ePSO), back-propagation learning (BP-Learning), and Chebyshev functional link neural networks (CFLNN). We have shown its effectiveness of classifying the unknown pattern using the datasets obtained from UCI repository. The computational results are then compared with other higher order neural networks (HONNs) like functional link neural network with a generic basis functions, Pi-Sigma neural network (PSNN), radial basis function neural network (RBFNN), and ridge polynomial neural network (RPNN).

scholarly journals A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Satchidananda Dehuri
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dimensional Space ◽  
Back Propagation ◽  
Functional Link ◽  
Learning Scheme ◽  
Chebyshev Functional ◽  
Hidden Layer ◽  
Functional Link Neural Networks ◽  
Functional Link Neural Network

A hybrid learning scheme (ePSO-BP) to train Chebyshev Functional Link Neural Network (CFLNN) for classification is presented. The proposed method is referred as hybrid CFLNN (HCFLNN). The HCFLNN is a type of feed-forward neural networks which have the ability to transform the nonlinear input space into higher dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO), back propagation learning (BP learning), and functional link neural networks (FLNNs). The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN) with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

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