A PID cascade main steam temperature control strategy based on radial basis function (RBF)neural network identification has been put forward

and a RBF-PID controller being constructed by combining RBF neural network with conventional PID cascade control.The said controller not only has the behavior of conventional PID cascade control

but also boasts the self-study ability of an intellegent controller

strengthening the system's adaptability to some uncertain factors.Results of simulation study show that the dynamic performance of RBF-PID control system is substantially superior to that of the convintional PID cascade control

can readily accommodate itself to the variation of object's parameters

having stronger robustness and self-adaptability.