학술논문
Wavelet neural network as a multivariate calibration method in voltammetric electronic tongues
Document Type
TEXT
Source
Neural network world: international journal on neural and mass-parallel computing and information systems | 2009 Volume:19 | Number:1
Subject
Language
English
Abstract
The paper presents a multi-output wavelet neural network (WNN) which, taking benefit of wavelets and neural networks, is able to accomplish data feature extraction and modeling. In this work, WNN is implemented with a feedforward one-hidden layer architecture, whose activation functions in its hidden layer neurons are wavelet functions, in our case, the first derivative of a Gaussian function. The network training is performed using a backpropagation algorithm, adjusting the connection weights along with the network parameters. This principle is applied to the simultaneous quantification of heavy metals present in liquid media, taking the cyclic voltammogram obtained with a modified epoxy-graphite composite sensor as departure information. The combination between processing tools and electrochemical sensors is already known as an electronic tongue.