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Model predictive control is a multivariable control algorithm that uses: an internal dynamic model of the process; a cost function J over the receding horizon; an optimization algorithm minimizing the cost function J using the control input u; An example of a quadratic cost function for optimization is given by:
An intermediate language is the language of an abstract machine designed to aid in the analysis of computer programs.The term comes from their use in compilers, where the source code of a program is translated into a form more suitable for code-improving transformations before being used to generate object or machine code for a target machine.
Internal control structure is a plan determining how internal control consists of these elements. [3] The concepts of corporate governance also heavily rely on the necessity of internal controls. Internal controls help ensure that processes operate as designed and that risk responses (risk treatments) in risk management are carried out (COSO II ...
Essentially, encapsulation prevents external code from being concerned with the internal workings of an object. Encapsulation allows developers to present a consistent interface that is independent of its internal implementation. As one example, encapsulation can be used to hide the values or state of a structured data object inside a class.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
The optimization is only based on the control performance (cost function) as measured in the plant. Genetic programming is a powerful regression technique for this purpose. [5] Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using reinforcement learning. [6]
In computer science, control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an imperative programming language from a declarative programming language.
An internal iterator is a higher order function (often taking anonymous functions) that traverses a collection while applying a function to each element. For example, Python's map function applies a caller-defined function to each element: