Laguerre neural network-based smart sensors for wireless sensor networks

Patra, J.C.; Bornand, Cédric; Meher, Pramod Kumar

doi:10.1109/imtc.2009.5168565

Cited by 25 publications

(14 citation statements)

References 13 publications

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“…β is the selffeedback gain of the hidden layer which is selected between 0 and 1. Laguerre orthogonal polynomials [20][21][22][23][24] are selected for activation function in the hidden layer. Laguerre orthogonal polynomials are indicated by L h ðxÞ, where h is the order of expansion and À1 o x o 1. m is the number of nodes.…”

Section: Configuration Of the Composite Recurrent Laguerre Orthogonalmentioning

confidence: 99%

“…Functional-link NNs [17][18][19], which show less computational complexity and faster convergence, have been implemented for performing the tasks of system identification and control in many nonlinear systems. Recently, combinations of Laguerre functional expansions and NNs, which have been used for highly nonlinear approximation, identification, compensation, and control of systems, have been proposed [20][21][22][23][24]. Aadaleesan et al [20] proposed a combination of a Laguerre filter and a wavelet network to approximate a memoryless nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

“…The Laguerre-functional-expansion feedforward NN, which employs Laguerre orthogonal polynomials in the activation functions of hidden neurons to identify models of chaotic time series, was proposed by Zou and Xiao [22]. Patra et al [23] proposed a computationally efficient Laguerre NN based on Laguerre functional expansions for auto-compensating for the environmental reliance and associated nonlinearity in intelligent sensors. Patra et al [24] proposed an intelligent skill involving a computationally efficient Laguerre NN for compensating for natural sensor nonlinearity and environmental effects.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Lin

2016

ISA Transactions

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Section: Configuration Of the Composite Recurrent Laguerre Orthogonalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Lin

2016

ISA Transactions

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“…Various ANN based methods like Multi Layer Perception Network (MLP) [6], Radial Basis Function Neural Network (RBF) [7], Wavelet Neural Network (WNN) [8] , Recurrent Neural Network (RNN) [9] and Functional Link Artificial Neural Network (FLANN) [10][11][12][13] are extensively used for stock market prediction due to their inherent capabilities to identify complex nonlinear relationship present in the time series data based on historical data and to approximate any nonlinear function to a high degree of accuracy. A major benefit of neural networks is that it incorporates prior knowledge in ANN to improve the performance of stock market prediction.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks

Bebarta¹,

Biswal²,

Dash³

2015

IJCIS

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A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. Although different types of polynomial functions have been used for low complexity neural network architectures earlier for stock market prediction, a comparative study is needed to choose the optimal combinations of the nonlinear functions for a reasonably accurate forecast. Further a recurrent version of the Functional link neural network is used to model more accurately a chaotic time series like stock market indices with a lesser number of nonlinear basis functions. The proposed PFLARNN model when trained with the well known gradient descent algorithm produces reasonable accuracy with a choice of range of weight parameters of the network. However, to improve the accuracy of the forecast further, the weight parameters of the recurrent functional neural network are optimized using an evolutionary learning algorithm like the differential evolution (DE). A comparison with other well known neural architectures shows that the proposed low complexity neural model can provide significant prediction accuracy for one day advance and speed of convergence using the International Business Machines Corp. (IBM) stock market indices.

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“…In [3] is proposed an ANN model called Laguerre Neural Network (LaNN). The goal of this model is to reduce the complexity and energy consumption in a node.…”

Section: Appliances Wsn and Annmentioning

confidence: 99%

Embedding a Neural Network into WSN furniture

Soares

Rocha

Barbosa

et al. 2010

2010 10th International Conference on Hybrid Intelligent Systems

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Wireless Sensor Networks (WSN) is an emergingtechnology that is developed with a large number of useful applications. On the other hand, Artificial Neural Networks (ANN) have found many successful applications in nonlinear system and control, digital communication, pattern recognition, pattern classification, etc. There are many similarities between WSN and ANN. For example, the sensor node itself can be seen as a neuron since the WSN application show characteristics such as distributed processing, massive parallelism, adaptively, inherent contextual information processing, fault tolerance and low computation. This paper examines the possibility of embedding ANN and WSN into a Smart Table. Prototypal results have shown that ANN models are good candidates for using it deployed into low cost System-on-a-Chip (SoC).

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Laguerre neural network-based smart sensors for wireless sensor networks

Cited by 25 publications

References 13 publications

RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks

Embedding a Neural Network into WSN furniture

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