Optimized Artificial Neural Network for Biosignals Classification Using Genetic Algorithm
| Autor: | Danilo Hernane Spatti, Fabio K. H. de Barros, María Eugenia Dajer, Victor Hideki Yoshizumi, Aron Alexandre Martins Lima |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
business.industry Computer science Reliability (computer networking) Computer Science::Neural and Evolutionary Computation 010401 analytical chemistry Energy Engineering and Power Technology Pattern recognition Topology (electrical circuits) 02 engineering and technology 01 natural sciences 0104 chemical sciences Computer Science Applications Wavelet packet decomposition Hybrid intelligent system Control and Systems Engineering Multilayer perceptron Pattern recognition (psychology) Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering SINAIS BIOMÉDICOS business |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| ISSN: | 2195-3899 2195-3880 |
| DOI: | 10.1007/s40313-019-00454-1 |
| Popis: | The artificial neural networks (ANNs) are increasingly being used to solve the problem of pattern recognition, but it is an arduous task for their designer to obtain the optimal topology to be used for ANN training since this is considered a very difficult problem. Even after several tests, the optimized topology may not be reached. A possible solution for this problem is the use of a hybrid intelligent system; an optimization technique is used together with the ANN in order to search for an optimized topology. This paper applies this concept, using the genetic algorithms for the optimization of the topology of a multilayer perceptron, used for the classification of wrist orientation, muscle contraction levels and subjective parameters of the voice. The data were preprocessed with wavelet packet transform. The tool presents promising results above 96% all the way up to 99% of total hits, with 98% and 90% reliability. |
| Databáze: | OpenAIRE |
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