The accuracy on time delay estimation given pairs of irregularly sampled time series is of great relevance in astrophysics. However the computational time is also important because the study of large data sets is needed. Besides introducing a new approach for time delay estimation, this paper presents a parallel approach to obtain a fast algorithm for time delay estimation. The neural network architecture that we use is general Regression Neural Network (GRNN). For the parallel approach, we use Message Passing Interface (MPI) on a beowulf-type cluster and on a Cray supercomputer and we also use the Compute Unified Device Architecture (CUDA™) language on Graphics Processing Units (GPUs). We demonstrate that, with our approach, fast algorithms can be obtained for time delay estimation on large data sets with the same accuracy as state-of-the-art methods.
Neural-Network-Based Time-Delay Estimation
