The determination of flare launching programs to use against pulse width modulating guided missile seekers via hidden Markov models

<abstract><p>In this paper, a fractional opposite-direction accumulating nonlinear grey Bernoulli Markov model (FOANGBMKM) is established to forecast the annual GDP of Huizhou city from 2017 to 2021. The optimal fractional order number and nonlinear parameters of the model are determined by particle swarm optimization (PSO) algorithm. An experiment is provided to validate the high fitting accuracy of this model, and the effect of prediction is better than that of the other four competitive models such as autoregressive integrated moving average model (ARIMA), grey model (GM (1, 1)), fractional accumulating nonlinear grey Bernoulli model (FANGBM (1, 1)) and fractional opposite-direction accumulating nonlinear grey Bernoulli model (FOANGBM (1, 1)), which proves the robustness of the opposite-direction accumulating nonlinear Bernoulli Markov model. This research will provide a scientific basis and technical references for the economic planning industries.</p></abstract>

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The determination of flare launching programs to use against pulse width modulating guided missile seekers via hidden Markov models

Cited by 4 publications

References 2 publications

Optimization of the jamming signal parameters for conical-scan systems

Optimization of the jamming signal parameters for conical-scan systems

House Price Forecast Based on Grey Fractional Markov Model

Huizhou GDP forecast based on fractional opposite-direction accumulating nonlinear grey bernoulli markov model

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