Data Driven Model

Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance

Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance

Master Swift Charts to Build Powerful, Data-Driven Interfaces in Your iOS Apps

Over recent years, many new technologies have been introduced to drive the digital transformation in the building maintenance industry.

This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory.

Learn how to create data-driven testing framework to automated test real time project (E-learning) in Java.

This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas.