Título: A new neural network concept for the control of nuclear reactor systems
Autores: Baptista Filho, Benedito D.; Cabral, Eduardo L. L.
Resumo: The novel approach to artificial neural networks based on the design of task-specific networks and on biological models of a neuron with multiple synapses developed by Baptista, Cabral and Soares (1998) was extended to accommodate external perturbations. The learning algorithm of this artificial neural network is an unsupervised training method based on the processes of habituation, sensitization and classical conditioning of human reflexes. In this paper, this new development is applied to the control of the fluid temperature at any point in a natural circulation loop. The learning and the action processes were implemented in a computer program. The thermal-hydraulics processes were also simulated. The natural circulation loop simulation model is based on physical equations and on experimentally identified parameters. The results show that besides the excellent learning capability and generalization, the new improvements are suitable to accommodate external perturbations so that the network is able to maintain the controlled variable within allowable limits even in the presence of strong perturbations.
Palavras-chave: Artificial Neural Networks; Unsupervised Learning Algorithms; Adaptive Control
Páginas: 19
Código DOI: 10.21528/lmln-vol1-no1-art2
Artigo em PDF: vol1-no1-art2.pdf
Arquivo BibTex: vol1-no1-art2.bib