Special Issue "Neural Networks and Sensors"

Assistant Editor
Ms. Laura Simon
MDPI, Kandererstrasse 25, CH-4057 Basel, Switzerland
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Guest Editor
Dr. Michael W. Retsky
Harvard Medical School, Childrens Hospital, Karp Family Research Labs 12, 300 Longwood Ave., Boston, MA 02115, USA
Website: http://hms.harvard.edu/WhitePagesPublic.asp?task=showperson&id=EVQyYGBFPzc=&a=hms&r=1&kw=
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Related Special Issue

Neural Networks and Sensors to be published in Sensors.

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All papers should be submitted to algorithms@mdpi.org. To be published continuously until the deadline and papers will be listed together at the special issue website.

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Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first two volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections).

neural networks, artificial intelligence, artificial neural networks, sensors

Title: Advances in Artificial Neural Networks – Methodological Development and Applications
Author: Yanbo Huang 1
Affiliation: 1 USDA-ARS JWDSRC, Application and Production Technology Research Unit, 141 Experiment Station Rd., Stoneville, MS 38776, USA
e-mail: Yanbo.Huang@ars.usda.gov
Abstract: Artificial neural networks as a major soft-computing technology have been extensively studied and applied in the last three decades. Research on backpropagation training algorithm for multilayer perceptron network has brought up development of other neural network training algorithms of other networks, such as radial basis function, recurrent networks, feedback network, and unsupervised Cohonen self-organizing network. These networks, especially the multilayer perceptron network with backpropagation training algorithm, gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve or replace the backpropagation algorithm. In recent years, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis. The purpose of the integration is to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. Most recently, the support vector machine has appeared as a method in parallel with artificial neural networks. The question remains if that is merely a new method or a product from the development of artificial neural networks. This paper will overview the history of development of artificial neural networks. Then, the structures and algorithms of artificial neural networks will be described. Furthermore, a number of advanced artificial neural networks will be introduced. The future of the development of artificial neural networks will be discussed with the support vector machine. With the concepts, network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the field of agricultural and biological engineering.
Keywords:Artificial neural networks, backpropagation, training algorithm, neuron-fuzzy, wavelet, support vector machine