Artificial neural networks (ANNs) are relatively new computational tools that have been extensively used in solving many complex real-world problems. The attractiveness of ANNs derives from their remarkable information processing features. Basically, an ANN is a network of adaptable nodes which perform learning from examples. ANNs are models based on brain like learning by architectures whose components resemble neurons. ANN is a multilevel parallel structure capable of generalisation and with fault and noise tolerance. This paper aims to introduce ANN-based computing and to serve as a useful practical guide for ANNs modellers. A generalised methodology for developing successful ANNs projects from conceptualisation to design and implementation is described. Two practical examples of medical and biological problems are also given.
Application of artificial neural networks in medicine and biology
GIACOMINI, MAURO;RUGGIERO, CARMELINA
2001-01-01
Abstract
Artificial neural networks (ANNs) are relatively new computational tools that have been extensively used in solving many complex real-world problems. The attractiveness of ANNs derives from their remarkable information processing features. Basically, an ANN is a network of adaptable nodes which perform learning from examples. ANNs are models based on brain like learning by architectures whose components resemble neurons. ANN is a multilevel parallel structure capable of generalisation and with fault and noise tolerance. This paper aims to introduce ANN-based computing and to serve as a useful practical guide for ANNs modellers. A generalised methodology for developing successful ANNs projects from conceptualisation to design and implementation is described. Two practical examples of medical and biological problems are also given.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
