Showing 3 results for Approximation
A. Moosavienia, K. Mohammadi,
Volume 1, Issue 1 (1-2005)
Abstract
In this paper we first show that standard BP algorithm cannot yeild to a uniform
information distribution over the neural network architecture. A measure of sensitivity is
defined to evaluate fault tolerance of neural network and then we show that the sensitivity
of a link is closely related to the amount of information passes through it. Based on this
assumption, we prove that the distribution of output error caused by s-a-0 (stuck at 0) faults
in a MLP network has a Gaussian distribution function. UDBP (Uniformly Distributed
Back Propagation) algorithm is then introduced to minimize mean and variance of the
output error. Simulation results show that UDBP has the least sensitivity and the highest
fault tolerance among other algorithms such as WRTA, N-FTBP and ADP. Then a MLP
neural network trained with UDBP, contributes in an Algorithm Based Fault Tolerant
(ABFT) scheme to protect a nonlinear data process block. The neural network is trained to
produce an all zero syndrome sequence in the absence of any faults. A systematic real
convolution code guarantees that faults representing errors in the processed data will result
in notable nonzero values in syndrome sequence. A majority logic decoder can easily detect
and correct single faults by observing the syndrome sequence. Simulation results
demonstrating the error detection and correction behavior against random s-a-0 faults are
presented too.
M. H. Refan, A. Dameshghi,
Volume 16, Issue 2 (6-2020)
Abstract
Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.
M. Khalaj-Amirhosseini, M. Nadi-Abiz,
Volume 16, Issue 2 (6-2020)
Abstract
Phase Perturbation Method (PPM) is introduced as a new phase-only synthesis method to design reflectarray antennas so as their sidelobe level is reduced. In this method, only the reflected phase of conventional unit cells are perturbed from their required values. To this end, two approaches namely the conventional Optimization method and newly introduced Phase to Amplitude Approximation (PAA) method are proposed. Finally, a reflectarray antenna is designed and fabricated to have a low sidelobe level and its performance is investigated.
