Recurrent radial basis function network for time-series prediction

This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to dynamic monitoring and prognosis. Based on the architecture of the conventional Radial Basis Function networks, the RRBFN have input looped neurons with sigmoid activation functions. These looped-neurons represent the dynamic memory of the RRBF, and the Gaussian neurons represent the static one. The dynamic memory enables the networks to learn temporal patterns without an input buffer to hold the recent elements of an input sequence. To test the dynamic memory of the network, we have applied the RRBFN in two time series prediction benchmarks (MacKey-Glass and Logistic Map). The third application concerns an industrial prognosis problem. The nonlinear system identification using the Box and Jenkins gas furnace data was used. A two-steps training algorithm is used: the RCE training algorithm for the prototype's parameters, and the multivariate linear regression for the output connection weights. The network is able to predict the two temporal series and gives good results for the nonlinear system identification. The advantage of the proposed RRBF network is to combine the learning flexibility of the RBF network with the dynamic performances of the local recurrence given by the looped-neurons.