Modelling and identification for non-uniformly periodically sampled-data systems

Gradient based and least squares based iterative algorithms for matrix equations AXB+CXTD=F

“…Finally, it is worth noting that the proposed iterative methods can be extended to study identification problems for multivariable systems [32,33], nonlinear systems [34][35][36][37][38], multirate systems [39][40][41] and non-uniformly sampled-data systems [42][43][44][45][46]. …”

Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

“…So far, several works have been carried out to predict MI with various types of modeling methods by scholars and practitioners [1][2][3][4][5][6][7][8][9]. Among them, taking the multiscale process characteristics into account, Shi [10,11] combined multiscale analysis and independent principal component with Radial Basis Function (RBF) neural network (NN) for MI modeling.…”

Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems

“…For example, Aihara, et al discussed parameter estimation problems of term structures modeled by stochastic hyperbolic systems [1] and of stochastic volatility models from stock data using particle filter application to AEX index [2]; Ding, et al presented self-tuning control algorithms for nonlinear dual-rate sampled-data systems [7] and for discrete-time systems using the multi-innovation identification theory [9][10][11][12][13][14][15][16][17]. The iterative methods are very important for solving matrix equations AX + X T B = F .…”