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The emgr framework is a compact open source toolbox for gramian-based model reduction and compatible with OCTAVE and MATLAB. KerMor: An object-oriented MATLAB© library providing routines for model order reduction of nonlinear dynamical systems. Reduction can be achieved via subspace projection and approximation of nonlinearities via kernels ...
Range ambiguity resolution is a technique used with medium pulse-repetition frequency (PRF) radar to obtain range information for distances that exceed the distance between transmit pulses. This signal processing technique is required with pulse-Doppler radar .
The mass-spring-damper model consists of discrete mass nodes distributed throughout an object and interconnected via a network of springs and dampers. This model is well-suited for modelling object with complex material properties such as nonlinearity and viscoelasticity. Packages such as MATLAB may be used to run simulations of such models. [1]
This corresponds to the following non-logarithmic gain model: =, where = / is the average multiplicative gain at the reference distance from the transmitter. This gain depends on factors such as carrier frequency, antenna heights and antenna gain, for example due to directional antennas; and = / is a stochastic process that reflects flat fading.
When sampling a function of variables, the range of each variable is divided into equally probable intervals. sample points are then placed to satisfy the Latin hypercube requirements; this forces the number of divisions, , to be equal for each variable. This sampling scheme does not require more samples for more dimensions (variables); this ...
In descriptive statistics, the range of a set of data is size of the narrowest interval which contains all the data. It is calculated as the difference between the largest and smallest values (also known as the sample maximum and minimum). [1] It is expressed in the same units as the data. The range provides an indication of statistical ...
If all the operators in a mathematical model exhibit linearity, the resulting mathematical model is defined as linear. A model is considered to be nonlinear otherwise. The definition of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them. For example, in a statistical linear model, it is ...
Generally these extensions make the estimation procedure more complex and time-consuming, and may also require more data in order to produce an equally precise model. [citation needed] Example of a cubic polynomial regression, which is a type of linear regression.