An Improved Recurrent Neural Network and Its Simulation

To deal with the weakness of the BP neural network in learning speed, an internally recurrent network model with bias cells is presented based on the Jordan and Elman neural networks. The weight-regulating method is developed based on BP algorithm. Simulations on fault diagnosis are performed with this neural network model. Experimental results show that the converging speed of this network model is faster than the traditional BP network and this model has a good practicability.