A Fast Parallel Algorithm for a Recurrent Neural Network

Because the recurrent BP algorithm suffers from the drawback of slow con-vergence, a new fast parallel learning algorithm for recurrent neural networks is pro-posed. First, the recursive predictive error(RPE) learning algorithm for recurrent neural networks is introduced, and its stability is demonstrated. Furthermore, in order to overcome the disadvantage of the centralized computing of RPE learning algorithm, a parallel structure algorithm is derived. In the new parallel learning algorithm, the com-putation is distributed to each neuron in the network, which is coherent with the mas-sively parallel nature of the network, and also convenient for hardware implementation. Simulation results show that better convergence performance of the proposed algorithm over the traditional recurrent BP algorithm. Meanwhile, theoretical analysis and simu-lation results show that the new parallel algorithm also cut lots of computational time compared with the RPE centralized computing algorithm.