Application of predictive coding in the evolution of artificial neural network

In this paper we propose a new encoding scheme utilises predictive coding technique in order to increase the efficiency of evolving artificial neural network. The predictor encodes the sample data fed to the system and the artificial neural network acts as the decoder. The latter is trained using a data model created via predictive coding, which is generated from the initial sample. Only the residual data output from the encoder is fed to the artificial neural network for authentication. Distributed and local processing has been simultaneously used in parallel and in synchrony. Comparison of the simulation results with those obtained using traditional methods such as selective biometric features shows an improvement in efficiency of up to 80% while utilising a lower complexity neural network.

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