CHAPTER 1 BACKGROUND OF STUDY System identification is a process of developing or improving the mathematical representation of a physical system using experimental data. It has been applied widely in aerospace engineering, mechanical engineering and structural engineering for active control, model validation and updating, conditional assessment, health monitoring and damage detection. System identification techniques can utilize both input and output data or can only include the output data. [1] The construction of system identification involves three basic entities that are a set of data, a model structure, and a rule by which the models can be assessed using the data. A set of data can sometimes gather during a specifically designed identification experiment. The user can decide which signals to measure and when the signals to be measured, together with the input signals too. A designed experiment is carried out as the user can choose the data that is the most informative and subject to constraints that may be at hand. [2] A set of candidate models can be obtained by specifying within which collection of models that the user is going to choose for a suitable one. This model choosing is the most difficult part of system identification. During this stage, the user must equip with prior knowledge with engineering intuition and insight. Sometimes, a model set is obtained after careful modeling. Then, basic physical laws and other well-established relationships are constructed to know the physical parameters in a model. Meanwhile, a black box can be obtained when standard linear models are employed without referring to the physical background. [2] The user can choose the best model from the set with the guidance from the data. Th... ... middle of paper ... ... result as the best global location. Besides the idea, a generalized orthonormal basis filters (GOBFs)-ARX model was constructed. Those methods showed lesser number of parameters was needed to be estimated and the model structure could be estimated together with the structure. Moreover, the conditions for closed loop system identifiability using routine operating data could be obtained by deriving the model structures. [11-13] Sometimes, people wonder is it necessary to excite all reference signals for the identification of a multivariable system operating in closed loop with a linear time-invariant controller. A research was done to determine the issue and found that that was not the case. A user can always choose a controller of sufficient complexity that will make the data informative with respect to that model structure. Every model has its disadvantages.
I do not predict that all of my results will follow a line of best fit
In conclusion table 10-1 on page 292 list the three types of models. These models provide
A maglev system is nonlinear and its open-loop response is unstable because of the nature of the magnetic forces used. PI controllers are based on a linearized model, so they do not compensate for certain variations. These variations can occur when a magnetic bearing experiences a working load change on the rotor during operation, ...
from them. The outliers in both models was not a factor in choosing the best
i.e. K ̇(t)=sY(t)-δK(t), L ̇(t)=nL(t) and A ̇(t)=gA(t) it is important to consider the new assumptions that concern the newly added inputs.
Which model do you accept and would utilize? Explain the model and why you feel it is the most acceptable, i.e.:
Today, engineers rely on damping systems to counteract nature's forces. There are many types of damping systems that engineers can now use for structures, automobiles, and even tennis rackets! This site focuses on damping systems in structures, mainly architectural variations of the tuned mass damper.
A model is a simplified representation of the structure and content of a phenomenon or system that describes or explains the complex relationships between concepts within the system and integrates elements of theory and practice (Creek et al 1993).
...alysis by use of symbols and graphics and establishing variances, a feature that lacks in RDSP as it involves intelligence and experience to make rapid decision.
In this section, the results of the research are presented. For each task carried out, the most important information obtained is presented.
The attribute set used in classification process is partitioned into two disjoint sets as test set and training set. The test set contains the attribute set with class predefined class label. Normally, the class tag arrives from prior experiential data. The test data can be represented as: (a1, a2, …, an; c), where ai is the attribute c represents the class. Even though the class tags of these testing data are unknown, the classes that these data belong can be predicted. As shown in the figure 5.1, a classification model can be considered as a black box that automatically assigns a class tag when a attribute set of unknown classes is provided. The classification step in data mining consist of two phases as given below
Systems approach is based on the fundamental principle that all aspects of a human problem should be treated together in a rational manner (Healy, 2005). I have divided this essay into relevant sections that cover an overview of systems ideas, general systems theory and ecological systems theory. This assignment will also include Germain and Gittermans life model, and it will be related back to the case study that has been provided. Limitations of systems theory will also be discussed.
Data is collected and the patterns are recognized, in order to understand the physical properties, and further to visualize the data as
Within the analysis phase a set of goals are needed within the domain. From this there are three perspectives which are taken; the object model the Ronald LeRoi Burback (1998) states “dynamic model, and a functional model. The object model represents the artifacts of the system. The dynamic model represents the interaction between these artifacts represented as events, states, and transitions. The functional model represents the methods of the system from the perspective of data flow.” After the analysis phase the system design phase takes place. Here the system is sub-categorized and appointed tasks and persistent data storage is established, also within this phase the architecture is formed. Lastly the object design phase starts and is where the implementation plan is established and algorithms and object classes are also
When electronic devices transfer information to another electronic device, the devices need to know when data flow is beginning and ending. This is done with signals for synchronization.i