Patent ID: 6401082
Filing Date: 2002-06-04
Classification: G06K,G06N

Abstract:
A robustness quantifiable autoassociative-heteroassociative neural network capable of synthesizing two sets of output signal data from a single input signal data set for predicting numerical fluctuations including stock market fluctuations, said network comprising:an encoding subnetwork comprising: a plurality of input signal receiving layers and nodes communicating numerical fluctuation data input signals to a projection space of said neural network, said input signals being from a source external to said neural network, a plurality of encoding nodes within said neural network input signal receiving layers forming one representative input signal; a decoding subnetwork connected to said projection space comprising a plurality of output signal transmitting layers communicating output signal data from said projection space of said neural network to an output; a plurality of decoding nodes within said output signal transmitting layers jointly transforming said numerical fluctuation input signal data set to a first predicted data set such as future stock market performance and a second data set replicating said input signal data set; a mean square error backpropagation neural network training algorithm; a source of training data such as historic stock market performance connected to said encoding subnetwork and applied to said mean square error backpropagation neural network training algorithm as a single set on said encoding subnetwork and generating two data sets from said decoding network; and an input signal data set and said second data set from said decoding subnetwork comparator block, said comparator block comparing accuracy of replication of said second data set from said decoding subnetwork to said input signal data set indicating robustness of said neural network.