STABILIZATION OF THERMAL NEUROCONTROLLERS

Autor:	K. T. Yang, Gerardo Diaz, Mihir Sen, Rodney L. McClain
Rok vydání:	2004
Předmět:	Work (thermodynamics) Mathematical optimization Artificial neural network Artificial Intelligence Computer science Linearization Control theory Control system Computation Stability (learning theory) Backpropagation Eigenvalues and eigenvectors
Zdroj:	Applied Artificial Intelligence. 18:447-466
ISSN:	1087-6545 0883-9514
DOI:	10.1080/08839510490442076
Popis:	This work deals with the stabilization of neurocontrollers used in thermal applications. The control system can be reduced to an iterative, nonlinear map in time, and its linearization enables a stability analysis. For simple neural networks with few neurons, the eigenvalues can be analytically calculated in terms of the synaptic weights and biases. However, unless care is taken, usual training methods can drive the network to weights and biases such that the corresponding control system is unstable. A modified backpropagation training method is developed here to simultaneously minimize the target error and increase the dynamic stability of the system. Numerical computations are used to analyze the stability of realistic neural networks and their corresponding control systems. The techniques developed are used on an experimental heat-exchanger facility where the stability results are tested and validated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::88ea39d8fcde8f70592d69b9770a3e40 https://doi.org/10.1080/08839510490442076 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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