Comments on linear feature extraction
Linear transformation for random-data vector z reduction to smaller dimension vector
Henderson, T. L.↗
Engineering topics
Lainiotis, D. G.
Publications and source records attributed to Lainiotis, D. G..
Comments on linear feature extraction
Linear transformation method for random data vector reduction by matrix algebra
Henderson, T. L.↗
Recursive algorithm for the calculation of the adaptive Kalman filter weighting coefficients.
Weighting coefficients calculations by recursive algorithm for designing optimal discrete adaptive Kalman filter
Lainiotis, D. G.↗
Optimal estimation in the presence of unknown parameters.
Adaptive optimal estimation of sampled stochastic process with finite state unknown parameters, using separation technique
Hilborn, C. G., Jr.↗
Sensitivity analysis of discrete Kalman filters.
Parameter sensitivity analysis of discrete suboptimal filters from optimal rmse estimate of actual system performance measure
Lainiotis, D. G.↗
Simplified parameter quantization procedure for adaptive estimation.
Iterative method for reducing filters required for optimal Kalman filter design maintaining parameter estimation accuracy
Lainiotis, D. G.↗
Application of state variable techniques to optimal feature extraction.
State variable techniques applied to optimal continuous linear feature extraction for binary Gaussian pattern recognition or detection problem
Henderson, T. L.↗
Digital matched filters for detecting Gaussian signals in Gaussian noise
Digital filters for detecting random signals in random noise
Henderson, T. L.↗
A nonlinear adaptive estimation recursive algorithm.
Nonlinear adaptive recursive algorithm for estimating unknown probability density given sequence of independent samples, deriving adaptive estimators of population moments
Lainiotis, D. G.↗
A nonlinear adaptive estimation recursive algorithm.
Nonlinear adaptive and recursive algorithm to estimate unknown probability density from independent sample sequence
Lainiotis, D. G.↗
Minimum mean square error approximation of unknown probability distribution functions.
Double stochastic approximation algorithm for minimizing mean square error in finite expansion of unknown probability distribution functions
Deuser, L. M.↗
Optimal estimation in the presence of unknown parameters.
Optimal estimation of sampled stochastic process with finite state unknown parameters
Hilborn, C. G., Jr.↗
Optimal adaptive filter realizations for sampled stochastic processes with an unknown parameter
Optimal adaptive filter for sampled stochastic processes with unknown parameter
Hilborn, C. G., Jr.↗