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- Title
Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function.
- Authors
de Campos Souza, Paulo Vitor; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Junio; Araujo, Vanessa Souza
- Abstract
The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.
- Subjects
PULSAR detection; WAVELETS (Mathematics); FUZZY neural networks; ROBUST control; PROBLEM solving; HYBRID systems
- Publication
International Journal on Artificial Intelligence Tools, 2019, Vol 28, Issue 1, pN.PAG
- ISSN
0218-2130
- Publication type
Article
- DOI
10.1142/S0218213019500039