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PREDICTION OF DETERMINISTIC PROCESSES
PREDICTION OF STATIONARY RANDOM PROCESSES
PREDICTING NONSTATIONARY RANDOM PROCESSES
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algorithm Alpha amplitude approximating attributes automatic averaging block diagram characteristic modes circuit cognitive system components constant corrector correlation function curves decrease determine deterministic processes deviation dicted digital computer discrete discuss distribution elements equations error criterion example exponential smoothing extrapolation feedback future values Gabor given impulse response increase input signals integral interpolator interval Kolmogorov's formula linear linear filter logical condition mathematical expectation meteorological method minimum mean-square error nonlinear filter nonstationary obtain open loop output parameters perceptron plant polynomial possible predicted values predicting filter predicting operator prediction accuracy prediction error prediction quality predictor probabilistic probability density probability density function problem prototypes pulse pure randomness quantities random function random processes random variable realization scalar product self-teaching shown in Fig situation solve square error stationary processes stationary random processes statistical Taylor series teaching sequence theory tion transfer function variation voltage weather forecasting weighting coefficients zero