Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
Grey-box radial basis function modelling
Neurocomputing
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The use of radial basis function (RBF) networks and least squares algorithms for acquisition and fine tracking of NASA's 70-m-deep space network antennas is described and evaluated. We demonstrate that such a network, trained using the computationally efficient orthogonal least squares algorithm and working in conjunction with an array feed compensation system, can point a 70-m-deep space antenna with root mean square (rms) errors of 0.1-0.5 millidegrees (mdeg) under a wide range of signal-to-noise ratios and antenna elevations. This pointing accuracy is significantly better than the 0.8 mdeg benchmark for communications at Ka-band frequencies (32 GHz). Continuous adaptation strategies for the RBF network were also implemented to compensate for antenna aging, thermal gradients, and other factors leading to time-varying changes in the antenna structure, resulting in dramatic improvements in system performance. The systems described here are currently in testing phases at NASA's Goldstone Deep Space Network (DSN) and were evaluated using Ka-band telemetry from the Cassini spacecraft.