Wavelet neural networks: A recent strategy for processing complex signals applications to chemistry

Juan Manuel Gutiérrez, Roberto Muñoz, Manel Del Valle

Research output: Chapter in BookChapterResearchpeer-review


In the last three decades, Artificial Neural Networks (ANNs) have gained increasing attention due to their wide and important applications in different areas of knowledge as adaptive tool for processing data. ANNs are, unlike traditional statistical techniques, capable of identifying and simulating non-linear relationships without any a priori assumptions about the data?s distribution properties. Furthermore, their abilities to learn, remember and compare, make them useful processing tools for many data interpretation tasks in many fields, for example in chemical systems or in the analytical field. Nevertheless, the development of new analytical instruments producing readouts of higher dimensionality and the need to cope with each time larger experimental data sets have demanded for new approaches in data treatment. All this has lead to the development of advanced experimental designs and data processing methodologies based on novel computing paradigms, in order to tackle problems in areas such as calibration systems, pattern recognition, resolution and recovery of pure-components from overlapped spectra or mixtures. This chapter describes the nature and function of Wavelet Neural Networks (WNNs), with clear advantages in topics such as feature selection, signal pre-processing, data meaning and optimization tasks in the treatment of chemical data. The chapter focuses on the last applications of WNNs in analytical chemistry as one of its most creative contributions from theoretical developments in mathematical science and artificial intelligence. Specifically, recent contributions from our laboratory showing their performance in voltammetric electronic tongue applications will be outlined and commented.© 2011 Nova Science Publishers, Inc. All rights reserved.
Original languageEnglish
Title of host publicationFocus on Artificial Neural Networks
Place of PublicationNova York (US)
Number of pages18
Publication statusPublished - 1 Dec 2011


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