A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Dehuri, Satchidananda

doi:10.1155/2011/107498

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…This section broadly describes the datasets that were used. In terms of the real-world datasets, the basic Iris dataset [38,39] was used (referred to as Dataset 1), where the input featuressepal length, sepal width, petal length, and petal width served as the input to the unsupervised algorithms. The class division served into three main classes-Setosa, Versicolour, Virginica.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis

et al. 2020

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In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.

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Section: A Dataset Descriptionmentioning

confidence: 99%

Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis

et al. 2020

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“…For this type of neural network a online and a off-line training algorithm has been defined with fast convergence properties. Actually, the Orthogonal Activation Function based neural network is a type of feedforward neural networks which have the ability to transform the nonlinear input space into higher dimensionalspace where linear separability is possible [8].…”

Section: Orthogonal Activation Function Based Neural Networkmentioning

confidence: 99%

Intelligent Embedded Systems

Odette¹

2018

Lecture Notes in Electrical Engineering

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This work describes a modelling method for a non-linear system which is based on a multi-point linear approximation for a model predictive control (MPC) purpose. The method is derived from artificial neural network techniques and exploits good properties of a Orthogonal Activation Function based Neural Network (OAF-NN). In this work, we describe a technique of a Explicit-MPC (EMPC) which performance, ease of implementation and extension for the hybrid system gives promising direction of a algorithm design for the embedded systems (ES). Proposed modelling procedure is characterized by fast training property and wide applicability in medical systems, automotive, power-electronics industry etc.. Linearity of the model allows to transform into Piecewise Affine (PWA) system structure and to exploit existing algorithms for a explicit model predictive control design. The applicability of the technique was evaluated on a hybrid system by means of existing software tools for MPC design.

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“…For this type of neural network online and off-line training algorithm has been defined with fast convergence properties. Actually, the OAF-NN is a type of feed-forward neural network having the ability to transform the nonlinear input space into a higher dimensional-space where linear separability is possible [16]. Many useful properties speak up for exploiting the OAF based neural network in system identification • Function approximation capability: Any arbitrary function can be approximated by the network to any desired level of accuracy.…”

Section: Neural Network For Input-output Mapping Of Nonlinear Symentioning

confidence: 99%

Smart technique for identifying hybrid systems

Stevek

Szűcs

Kvasnica

et al. 2012

2012 IEEE 10th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

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The paper describes a system identification method for a nonlinear system based on a multi-point linear approximation. We show that under mild assumptions, the task can be transformed into a series of one-dimensional approximations, for which we propose an efficient solution method based on solving simple nonlinear programs (NLPs). The approach provides identification of nonlinear systems in a polynomial model structure (ARX,OE,BJ) from input-output data. The approximation is based on a neural network modelling procedure. The proposed modelling procedure is characterized by fast training, adjustable accuracy and reduced complexity of the final model. The modelling technique is widely applicable in automotive, power electronics, computer graphics, etc..

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A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Cited by 11 publications

References 40 publications

Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis

Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis

Intelligent Embedded Systems

Smart technique for identifying hybrid systems

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