Saturday 29 October 2016

Simulation in MATLAB Assignment help by Assignmentscholar.com

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Mathematical modeling is the art of capturing natural phenomenon of real life in the mathematical equation. For example the reading of bike speedometer shows 70 km/hr. There are ways through which we can develop a mathematical model. The process for developing a mathematical model is as follows:
1. Finding a real world problem.
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3. Applying the mathematical knowledge that you have to reach mathematical conclusions.
4. Comparing the data obtained as predictions with real data. If the data are different, the process is restarted.
The mathematical modeling is divided into two models, discrete and continuous model.
Discrete model: A discrete model treats objects as discrete, such as the particles in a molecular model. A clock is an example of discrete model because the clock skips to the next event start time as the simulation proceeds.
Continuous Model: A continuous model represents the objects in a continuous manner, such as the velocity field of fluid in pipe or channels, temperatures and electric field. Although there are some limitations of mathematical modeling. Till today many mathematical modeling have been developed and applied successfully to understand and get an insight into thousands of situations. Sone of the subjects like mathematical physics, mathematical economics, operation research etc are synonyms with mathematical modeling.
The major courses to study in mathematical modeling are Differential equation based models, Dimensional analysis, Stochastic models, Partial differential equations, stiff equations, delay differential equations, Compartment models: population dynamics, infectious disease models, Model scaling, dimensionless forms and ?-numbers, Probability distribution, random draws from distributions, Central limit theorem etc.

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Friday 28 October 2016

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  • Evaluation of the response of a discrete-time, linear time-invariant system by convolution, Graphical evaluation of convolution, Frequency response of discrete-time LTI systems, Steady-state sinusoidal response, Z-transform, some standard z-transform pairs, properties of z-transforms, transfer function of a discrete-time LTI system, Unit impulse
  • Unit step responses of Discrete-time LTI systems, Poles , Zeros, z-plane, stability of discrete-time causal LTI systems, Deterministic data, random data, Basic description properties , mean, variances, probability distribution function, autocorrelation, joint properties of random data, cross-correlation function

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