Signal Reconstruction Using Evolvable Recurrent Neural Networks

Abstract

Accurate reconstruction of under-sampled data plays an important role in wireless transmission of signals. A novel approach to reconstruct randomly missing data based on interpolation and machine learning technique i.e. Cartesian genetic programming evolved recurrent neural network (CGPRNN) is proposed in this research. Although feed-forward Neural networks have been very successful in signal processing fields in general with recurrent neural networks having an edge where system with memory is priority. Recurrent neural networks not only provide non-linearity but also non-Markovian state information. The proposed method is used for reconstruction of lost samples in audio signal which are non-stationary in nature through accurate predication. Simulation results are presented to validate the performance of CGPRNN for accurate reconstruction of distorted signal. The error rate of 12% for 25% missing data and 18% for 50% distorted data has been achieved where the system has low confidence in its predication.