Abstract
In this work the novel approach to artificial neural networks based on the design of task-specific networks and on a neuron model with multiple synapses developed by Baptista, Cabral and Soares (1998) is extended to accommodate external perturbations. As an example of this new development t he neural network is applied to control the fluid temperature of a natural circulation loop. The learning and the action processes are made through simulations. The natural circulation loop simulation model i s based on physical equations and on experimentally identified p arameters. The results s how that besides the e xcellent l earning capability and g eneralization, the new improvements are suitable to accommodate e xternal perturbations s o that t he network is able to maintain the controlled variable within allowable limits even in the presence of strong perturbations.
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