The quadratic approximation is a three dimensional analogue of the two dimensional Padé approximation. A determinantal expression for the polynomial coefficients of the quadratic approximation is given. A recursi...
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The quadratic approximation is a three dimensional analogue of the two dimensional Padé approximation. A determinantal expression for the polynomial coefficients of the quadratic approximation is given. A recursive algorithm for the construction of these coefficients is derived. The algorithm constructs a table of quadratic approximations analogous to the Padé table of rational approximations.
A new recursive algorithm for adaptive Kalman filtering is proposed. The signal state-space model and its noise statistics are assumed to depend on an unknown parameter taking values in a subset [', '] of Rs. ...
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A new recursive algorithm for adaptive Kalman filtering is proposed. The signal state-space model and its noise statistics are assumed to depend on an unknown parameter taking values in a subset [', '] of Rs. The parameter is estimated recursively using the gradient of the innovation sequence of the Kalman filter. The unknown parameter is replaced by its current estimate in the Kalman-filtering algorithm. The asymptotic properties of the adaptive Kalman filter are discussed.
This paper presents a new method of converting a curve into chain-code representation. At every step of chain encoding, a predictive searching is conducted so that an optimal code direction is determined. Optimization...
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This paper presents a new method of converting a curve into chain-code representation. At every step of chain encoding, a predictive searching is conducted so that an optimal code direction is determined. Optimization is performed on the basis of a minimum mean-square-error criterion. This approach works very well for complex geometrical configuration with overlapping, crossing, and touching characteristics. We have applied this method with great success to computer analysis of chromosomes.
A recursive algorithm of stochastic approximation type is derived for the estimation of space-dependent parameters in a class of distributed parameter systems driven by random inputs. The algorithm uses noisy observat...
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A recursive algorithm of stochastic approximation type is derived for the estimation of space-dependent parameters in a class of distributed parameter systems driven by random inputs. The algorithm uses noisy observations taken over at a finite number of discrete points located in the spatial domain. Stochastic convergence of the estimates is proved. Simulation studies illustrate the good convergence property of the algorithm. The algorithm is much more simple both in structure and implementation and less restrictive on system structure to apply the algorithm, compared to the algorithm of Kubrusly and Curtain (1977).
This paper presents a new recursive algorithm for computing bounds on the reliability of a directed, source-sink network whose arcs either function or fail with known probabilities. The reliability is the probability ...
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The Turing machine model is extended to allow for recursive calls and the basic theory of these machines is developed. The model is also used to study the following additional topics: The time and storage needed to im...
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This paper considers the computation problems in feature selection. A recursive computation procedure is presented for feature selection and ordering by using the indirect measures such as the Bhattacharyya distance a...
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This paper considers the computation problems in feature selection. A recursive computation procedure is presented for feature selection and ordering by using the indirect measures such as the Bhattacharyya distance and mutual information. Both binary and quantized measurements are considered. Supporting computer results are provided.
The problem of pole assignment in linear, multivariable systems using an unconstrained-rank feedback matrix is considered. The effect of output feedback of unspecified rank on the characteristic polynomial of a multiv...
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The problem of pole assignment in linear, multivariable systems using an unconstrained-rank feedback matrix is considered. The effect of output feedback of unspecified rank on the characteristic polynomial of a multivariable system is first studied. The results are then used to derive a recursive algorithm for pole assignment without any restrictions on the rank of the output feedback matrix used. The method is based on the pseudoinverse concept for obtaining least-squares solutions of sets of linear equations, and is computationally efficient.
A method based on the matrix pseudoinverse is presented for the online identification of discrete-time systems of known order. recursive algorithms are described which provide minimum-norm estimates of the parameter v...
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A method based on the matrix pseudoinverse is presented for the online identification of discrete-time systems of known order. recursive algorithms are described which provide minimum-norm estimates of the parameter vector when insufficient data are available, and least-squares estimates with adequate data. These estimates can be updated easily with each pair of additional input-output data, as matrix inversion is not required. When the order of the system is not known, it may be determined offline using one of the two methods described. A recursive algorithm for calculating the residual error is also derived. A number of examples are given to illustrate the usefulness of the methods.
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