A smoothed variant of the EM algorithm is given for simultaneous training of the first layer and the output layer globally in the normalized radial basis function (NRBF) nets and extended normalized RBF nets (ENRBF), together with a Bayesian Ying-Yang learning criterion for the selection of basis function numbers. Moreover, a hard-cut fast implementation and an adaptive algorithm have also been proposed for speeding up the training and for handling time varying in real time nonlinear signal learning and processing. A number of experiments are made on foreign exchange prediction and trading investments.
['Lei Xu']
Adaptive RBF net algorithms for nonlinear signal learning with applications to financial prediction and investment