Nonlinear system identificationSystem identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more.Data-driven control systemData-driven control systems are a broad family of control systems, in which the identification of the process model and/or the design of the controller are based entirely on experimental data collected from the plant. In many control applications, trying to write a mathematical model of the plant is considered a hard task, requiring efforts and time to the process and control engineers.
Control theoryControl theory is a field of control engineering and applied mathematics that deals with the control of dynamical systems. The aim is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any delay, overshoot, or steady-state error and ensuring a level of control stability; often with the aim to achieve a degree of optimality.
Mathematical modelA mathematical model is an abstract description of a concrete system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in many fields, including applied mathematics, natural sciences, social sciences and engineering. In particular, the field of operations research studies the use of mathematical modelling and related tools to solve problems in business or military operations.
SystemA system is a group of interacting or interrelated elements that act according to a set of rules or set of constraints to form a unified whole. A system, surrounded and influenced by its environment, is described by its boundaries, structure and purpose and is expressed in its functioning. Systems are the subjects of study of systems theory and other systems sciences.
Design of experimentsThe design of experiments (DOE), also known as experimental design, refers to the construction of procedures that attempt to explain how changes in one aspect of a system will lead to changes in other aspects of a system. In general, the design of experiments involves decisions about which aspects of the system to change and which to control based on hypotheses about the sources of variance in the aspects of the system considered by the experimenter.
Physical systemA physical system is a collection of physical objects under study. The collection differs from a set; the objects must coexist and have some physical relationship. In other words, it is a portion of the physical universe chosen for analysis. Everything outside the system is known as the environment, which is ignored except for its effects on the system. The split between system and environment is the analyst's choice, generally made to simplify the analysis.
Dynamical systemIn mathematics, a dynamical system is a system in which a function describes the time dependence of a point in an ambient space, such as in a parametric curve. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, the random motion of particles in the air, and the number of fish each springtime in a lake.
Optimal experimental designIn the design of experiments, optimal experimental designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statisticalcriterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. In the design of experiments for estimatingstatistical models, optimal designs allow parameters to be estimated without bias and with minimum variance.
Regression analysisIn statistical modeling, regression analysis is a statistical method for estimating the relationship between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more independent variables (often called regressors, predictors, covariates, explanatory variables or features).
Black boxIn science, computing, and engineering, a black box is a system which can be viewed in terms of its inputs and outputs (or transfer characteristics), without any knowledge of its internal workings. Its implementation is "opaque" (black). The term can be used to refer to many inner workings, such as those of a transistor, an engine, an algorithm, the human brain, or an institution or government.
StatisticsStudy of collection and analysis of dataThe normal distribution, a very common probability density, is used extensively in inferential statistics.Scatter plots and line charts are used in descriptive statistics to show the observed relationships between different variables, here using the Iris flower data set.Statistics (from German: Statistik, orig.*As an Amazon Associate I earn from qualifying purchases.