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This paper presents multiple observer design for nonlinear chaotic systems with unknown inputs in multiple model approach.

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GFA in this study serves to select descriptors that were applied as input in multiple linear regression (MLR), multiple non-linear regression (MNLR) and applicability domain (AD).

Both steady state input multiplicity (i.e., multiple values of input resulting in one value of output) and output multiplicity (i.e., one value of input resulting in multiple values of output) are possible.

Selected features can be introduced as inputs in a multiple regression model.

These two methods are termed the 'individual model ensemble' and the other 'model ensemble Monte Carlo simulation' respectively, and both provide multiple change predictions derived from multiple AOGCMs as input, resulting in multiple realizations of possible future changes in coral cover using a variety of model parameters.

GSNAF has been shown to offer improved accuracy in benchmark classification scenarios and provide enhanced modeling accuracy in single input single output as well as in multiple input multiple output dynamic system identification cases.

Such programmability of nonreciprocity in a multi-port network will inspire new applications in multiple input multiple output (MIMO) communication systems.

In recent years, Monte Carlo simulations for propagating uncertainty in multiple input variables have been developed for all five proxy systems11,55,59,77.

Double Spatial Modulation (DSM) is a recently proposed high rate Index Modulation (IM) scheme, designed for use in Multiple Input Multiple Output (MIMO) wireless systems.

A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper.

The proposed modulator can be used in multiple input and multiple output (MIMO) communication systems, where the receiver needs to rearrange the data in the correct order.

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