behavior of the neural networks 450 480 converges acceptably closely to the behavior of the actual furnace 120 and prior art regulator 130 the networks are said to be sufficiently 34 trained 34 to be installed in the operational configuration because each network 39 s behavior is completely defined by the weights in the neurons once the networks have been trained the weights may be stored then when the regulator 100 is installed in its operational configuration to control furnace 120 the weights may be reloaded into the networks so that they will initially behave in a known and acceptable manner the training configuration 500 of the regulator 100 is shown in greater detail in fig1 since fig1 is similar to fig5 and since fig5 has been previously described in detail only those aspects of the training configuration 500 of fig1 which are different from fig5 will be described in order to obtain information on the behavior of the prior art regulator 130 the output of the regulator is provided to a data acquisition system 510 on lead 518 this data acquisition system 510 performs the essentially same functions as the data acquisition system 410 used to collect furnace operating state information and may be implemented using the same resources the data acquisition system 510 presents its output as a regulator state vector on lead 512 the regulator state 512 produced by the data acquisition system 510 is stored in a buffer 514 and made available to other regulator components as the present regulator state r3 on lead 516 each time a new regulator state vector r3 on lead 516 becomes available the previous regulator state is provided to a buffer 520 via lead 518 and is made available as vector r2 on.