Showing papers by "Kenneth Steiglitz published in 1968"
TL;DR: A practical adaptive step size random search algorithm is proposed, and experimental experience shows the superiority of random search over other methods for sufficiently high dimension.
Abstract: Fixed step size random search for minimization of functions of several parameters is described and compared with the fixed step size gradient method for a particular surface. A theoretical technique, using the optimum step size at each step, is analyzed. A practical adaptive step size random search algorithm is then proposed, and experimental experience is reported that shows the superiority of random search over other methods for sufficiently high dimension.
261 citations
TL;DR: This paper presents a general approach to the derivation of series expansions of second-order wide-sense stationary mean-square continuous random process valid over an infinite-time interval based on the integral representation of such processes.
Abstract: This paper presents a general approach to the derivation of series expansions of second-order wide-sense stationary mean-square continuous random process valid over an infinite-time interval. The coefficients of the expansion are orthogonal and convergence is in the mean-square sense. The method of derivation is based on the integral representation of such processes. It covers both the periodic and the aperiodic cases. A constructive procedure is presented to obtain an explicit expansion for a given spectral distribution.
20 citations
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11 citations
TL;DR: In this paper, sufficient conditions are given for an interpolation filter to have an impulse response that vanishes outside a finite interval of the time axis, that is to have a finite memory.
Abstract: Sufficient conditions are given for an interpolation filter to have an impulse response that vanishes outside a finite interval of the time axis, that is to have a finite memory. These conditions are that the transfer function be of the form G(s)/G(z) , where G(s) is proper, rational, and has poles limited to the strip |Im s| ; and where 1/G(z) is a polynomial. The filters R_{mp} are included in this class, and these are characterized by the fact that their effect is to interpolate an (m + p - 1) -order polynomial in each interval through p past and m future points. The interpolation filters described can be used to derive digital filters that approximate an arbitrary linear timeinvariant continuous-time operator. It is shown that in the case of integration, the R_{mp} filters lead to well-known Lagrangian integration formulas.
6 citations
TL;DR: In this paper, the prediction error at an instant k is E(R) = 3t(k + 1)-~ (k 4-1) or E(2) = B(z)X(z)-Z X (Z) =-e-X(2), =-sY(2).
Abstract: Thus The prediction error at anfinstant k is E(R) = 3t(k + 1)-~ (k 4-1) or E(2) = B(z)X(z)-Z X (Z) =-e-X(2) =-sY(2)
3 citations