초록 ▼

A signal processing system and method for accomplishing signal processing using a neural network that incorporates adaptive weight updating and adaptive pruning for tracking non-stationary signal is presented. The method updates the structural parameters of the neural network in principal component

A signal processing system and method for accomplishing signal processing using a neural network that incorporates adaptive weight updating and adaptive pruning for tracking non-stationary signal is presented. The method updates the structural parameters of the neural network in principal component space (eigenspace) for every new available input sample. The non-stationary signal is recursively transformed into a matrix of eigenvectors with a corresponding matrix of eigenvalues. The method applies principal component pruning consisting of deleting the eigenmodes corresponding to the smallest saliencies, where a sum of the smallest saliencies is less than a predefined threshold level. Removing eigenmodes with low saliencies reduces the effective number of parameters and generally improves generalization. The output is then computed by using the remaining eigenmodes and the weights of the neural network are updated using adaptive filtering techniques.

대표청구항 ▼

[ What is claimed is:] [1.] A neural network having a plurality of weights for receiving a sequence of signal inputs x.sub.t,x.sub.t+1, x.sub.t+2 . . . , each input x.sub.t comprising n signal components x.sub.1 (t), x.sub.2 (t-1), . . . , x.sub.n (t-(n-1)) and for generating an output signal that a

[ What is claimed is:] [1.] A neural network having a plurality of weights for receiving a sequence of signal inputs x.sub.t,x.sub.t+1, x.sub.t+2 . . . , each input x.sub.t comprising n signal components x.sub.1 (t), x.sub.2 (t-1), . . . , x.sub.n (t-(n-1)) and for generating an output signal that anticipates the behavior of said input signal for a number of time samples ahead, said neural network comprising:transformation means for transforming a set of n signal inputs into a set of principal components having a saliency assigned to each of said principal component;pruning means, coupled to said transformation means, for pruning a number of said principal components that correspond to the smallest saliencies, where the number of said principal components is limited by a sum of said saliencies of said pruned principal components to be less than or equal to a predefined threshold level, leaving a remaining set of principal components;first computing means, coupled to said pruning means, for computing the output signal using said set of remaining principal components: andwherein said neural network an updating means, coupled to said first computing means, for updating the weights of the neural network adaptively based on an error between a target output and the output signal.