scholarly journals Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images

2021 ◽  
Author(s):  
Manish Kumar ◽  
Sudhansu Kumar Mishra ◽  
Justin Joseph ◽  
Sunil Kumar Jangir ◽  
Dinesh Goyal
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Ultrasound Images ◽  
Functional Link ◽  
Functional Link Neural Network

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).

Multi-kernel based Random Vector Functional Link Neural Network for Short-term Prediction of Wind Speed

2021 ◽  
Author(s):  
Sniedha Sarangi ◽  
Prdipta Kishore Dash ◽  
Badri Narayan Sahoo ◽  
Ranjeeta Bisoi
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Random Vector ◽  
Short Term ◽  
Functional Link ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Functional Link Neural Network

Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

2017 ◽  
Vol 13 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tutut Herawan ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Artificial Bee Colony ◽  
Classification Problem ◽  
Functional Link ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Learning Scheme ◽  
Feed Forward Network ◽  
Functional Link Neural Network

Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.

scholarly journals Stacked autoencoder based deep random vector functional link neural network for classification

2019 ◽  
Vol 85 ◽  
pp. 105854 ◽  
Author(s):  
Rakesh Katuwal ◽  
P.N. Suganthan
Keyword(s):  
Neural Network ◽  
Random Vector ◽  
Functional Link ◽  
Stacked Autoencoder ◽  
Functional Link Neural Network
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