Neural network identification and control in the presence of noise

This paper examines the performance of a control system design in the presence of noise. An architecture from the seminal work of Narendra and Parthasarathy (1990) is modified to institute recurrence in the neural net and the recurrent system performance is compared to the feedforward system response. The process of comparing the feedforward to the recurrent system is repeated for ten networks each having unique weights. The weights of each network are processed to obtain certain previously derived performance measures. The results of the experiments show that bias and variance performance of neural network control and identification systems can be improved by using the performance measures in the design process.