Software Is Tightly Integrated With The Matlab Environment

Cashman, G. B., Rosenblatt, H. J., & Shelly, G. B. (2013). Systems Analysis and Design (10th ed.). Boston, MA: Thomson - Course Technology.

Dependent variables, which are obtained from the solution of the model equations after decision variables are set by the decision maker (i.e., optimizer).

ways, does the model agree with existing data, can the model be used to make physical predictions, what

As long as the field of mathematics has existed, people have been searching for shortcuts to eliminate the monotony and difficulty of calculating figures accurately. As a result, human beings began to develop new technologies to simplify this process. In ancient history, the abacus was a useful device in calculating simple numbers requiring addition and subtraction. In the seventeenth century, the first mechanical calculators were able to perform multiplication and division through repetitions of addition and subtraction. Calculators were then programmed in order to multiply and divide automatically. From these early devices emerged the first computers and calculators, which were originally intended to calculate figures. Now, modern computers are expected to perform a variety of functions, outside of calculations, quickly and effectively. However, back in the time of abacuses, an ancient Greek mathematician was discovering the formulas and primitive mechanical devices that have evolved into the current state of computing technology.

Simulation Models are dynamic models; Dynamic modeling in organizations is the collective ability to understand the implications of change over time. This skill lies at the heart of successful strategic decision process. The availability of effective visual modeling and simulation enables the analyst and the decision-maker to boost their dynamic decision by rehearsing strategy to avoid hidden pitfalls.

The model can be applied to many situations but, mostly such framework is used in order to:

This is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both "programming in the small" to rapidly create quick and dirty throw-away programs, and "programming in the large" to create large and complex application programs.

Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems.

It is combination of Principal component analysis (PCA), Discrete cosine transform (DCT), Template matching using correlation (Corr) and partitioned iterative function system (PIFS).

Abstract—regression analysis is a statistical process that is used to estimate the connections between the variables, relating the dependent and the independent variables. Many types of regression are used such as linear regression and quadratic regression. The goal of this lab is to become familiar with Matlab and M-files, and how to create plots along with how to enter and alter data. The regression analysis is sued to reflect the data behavior in a plot format to give visualization of it. The quadratic type of regression is used for better fitting.

Relevant numerical techniques, which have been done with the help of MATLAB routines, are applied to solve the arising optimization problem and to find the optimum parameters of the TMD. For a given mass ratio, µ, one can assume different values of the frequency ratio, f, and for each frequency ratio assuming a range of damping factor ζ2 of the TMD and estimate the optimum parameters that minimize a certain desired output. Fig. 8 is an example of the numerical optimization conducted to estimate the optimal frequency ratio and damping factor of the TMD for two different mass ratios under wind loads modeled as white-noise. The optimization is based on the minimization of the displacement of the primary structure. In this numerical optimization, the responses of the primary structure are normalized, which means that the response obtained with the TMD when attached to the structure is divided by the corresponding response obtained without the TMD. The optimal values of the frequency ratio and the damping factor of the TMD are written on the subfigures. It is shown that a TMD with 1% mass ratio can provide a significant reduction in the displacement response of the primary structure. The reduction in the displacement depends very much on the tuning frequency and the damping ratio of the TMD. By increasing the mass ratio from 1% to 5%, the displacement response of the primary structure is reduced. However, the TMD with 5% mass ratio is more robust to the changes in the frequency

The EDA Simulator Link provides a co-simulation interface between Simulink and HDL simulators-ModelSim. EDA Simulator Link enables us to use MATLAB code and Simulink models as a test bench that generates stimulus for an HDL simulation and analyzes the simulation’s

It is used to perform data flow functions like reading, validating, refining, transforming, writing data to data sources.

continuous and discrete dynamics. When designing a control system the first step is to define

L’agence dispose de plusieurs logiciels pour gérer les opérations automatiques, des logiciels comme Portail et Station NACOM.