Spectral deconvolution by simulated annealing

Hoffman, Ralph E.; Levy, George C.

doi:10.1016/0022-2364(89)90204-7

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…Other analytical applications of SA have included calibration sample selection (19), optimizing calibration designs in multicomponent analysis (20), NMR spectral deconvolution (21), and nonlinear least-squares curve fitting of noisy fluorescence decay data (22). The latter used simulated annealing to find the region containing the globally optimal parameter estimates, which were then precisely located using the more traditional sequential simplex approach.…”

Section: Miscellaneousmentioning

confidence: 99%

Optimization by Simulated Annealing Theory and Chemometric Applications

Curtis¹

1994

J. Chem. Educ.

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Section: Miscellaneousmentioning

confidence: 99%

Optimization by Simulated Annealing Theory and Chemometric Applications

Curtis¹

1994

J. Chem. Educ.

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“…Primarily used in combinatorial problems, like the traveling salesman problem (341, chip routing, or time schedule optimization (35), increasing computer power now allows use of these ideas for the optimization of continuous functions too (36)(37)(38)(39).…”

Section: Introductionmentioning

confidence: 99%

“…Heuristic optimization processes such as genetic algorithms (GA) (24)(25)(26)(27)(28)(29)(30) and simulated annealing (SA) (31,32) offer promising alternatives, since they reportedly avoid these drawbacks and explore a larger part of the landscape in a reasonable computing time (33). Primarily used in combinatorial problems, like the traveling salesman problem (341, chip routing, or time schedule optimization (35), increasing computer power now allows use of these ideas for the optimization of continuous functions too (36)(37)(38)(39).…”

Section: Introductionmentioning

confidence: 99%

Heuristic optimization algorithms applied to the quantification of spectroscopic data

Weber

Duc

Meier

et al. 1998

Magnetic Resonance in Med

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The quantification of in vivo MR spectra imposes severe problems because of low spectral resolution and poor signal-to-noise ratio. Maximum likelihood methods are often applied. However, with conventional spectrum analysis procedures, the search for a global minimum in a multidimensional space often terminates in only a local minimum. Heuristic optimization procedures are able to circumvent this difficulty. Two approaches, the genetic algorithm and the simulated annealing, have been adapted to the quantification of MR spectra. For evaluation purposes, the procedures have been applied to synthetic and in vivo spectra with different noise levels. They both allowed a reliable spectrum quantification. The areas of most peaks were quantified reproducibly, although in some cases, the discrimination between spectroscopically almost identical metabolites (e.g., glutamate and glutamine) was not completely satisfactory. The two algorithms are found to be valuable alternative methods in the quantification of in vivo MR spectra.

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“…Simulated annealing method has recently been utilized to reduce the difficulty of guessing initial values. However, this method requires very long processing time [5].…”

Section: Introductionmentioning

confidence: 99%

Accuracy enhancement of parameter estimation for noisy NMR spectra

1991

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Ah ARMA(M,M--1) modeling is implemented by the multi-stage linear least squares. This modeling technique is applied to the analysis of noisy NMR signals. A new method is proposed to eliminate the spu¡ peaks by utilizing the phase angle difference between the real and imaginary data. The proposed method is tested to simulated and experimental 170 NMR spectra with seven peaks. The results are compared to those by the autoregressive (AR) Householder t¡ decomposition (QRD). The proposed ARMA(M,M--1) model yields more accurate and consistent parameter estimation than AR model in the noisy spectra.

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Spectral deconvolution by simulated annealing

Cited by 9 publications

References 25 publications

Optimization by Simulated Annealing Theory and Chemometric Applications

Optimization by Simulated Annealing Theory and Chemometric Applications

Heuristic optimization algorithms applied to the quantification of spectroscopic data

Accuracy enhancement of parameter estimation for noisy NMR spectra

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