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Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
The direct data-driven methods allow to tune a controller, belonging to a given class, without the need of an identified model of the system. In this way, one can also simply weight process dynamics of interest inside the control cost function, and exclude those dynamics that are out of interest.
Data analysis focuses on extracting insights and drawing conclusions from structured data, while data science involves a more comprehensive approach that combines statistical analysis, computational methods, and machine learning to extract insights, build predictive models, and drive data-driven decision-making. Both fields use data to ...
Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope ...
While data-driven design does prevent coupling of data and functionality, in some cases, data-driven programming has been argued to lead to bad object-oriented design, especially when dealing with more abstract data. This is because a purely data-driven object or entity is defined by the way it is represented. Any attempt to change the ...
Dynamic Data Driven Applications Systems ("DDDAS") is a paradigm whereby the computation and instrumentation aspects of an application system are dynamically integrated with a feedback control loop, in the sense that instrumentation data can be dynamically incorporated into the executing model of the application (in targeted parts of the phase-space of the problem to either replace parts of ...
Surrogate models are constructed using a data-driven, bottom-up approach. The exact, inner working of the simulation code is not assumed to be known (or even understood), relying solely on the input-output behavior. A model is constructed based on modeling the response of the simulator to a limited number of intelligently chosen data points.
The Data-driven design describes structures that reside within the software. Attributes and relationships between data objects dictate the choice of data structures. The architecture design uses information flowing characteristics, and maps them into the program structure. The transformation mapping method is applied to exhibit distinct ...