Performance Driven Journal
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Performance-Driven Thinking
Performance Driven Thinking is a challenging journey that will encourage you to embrace the greatest performance of your life. During this journey, you will experience the call to perform in both your personal and professional existence. The truth is you were born to perform. But the question is at what level will your performance take place? During this journey you will learn how your life from the day you were born until the day you started reading this book was all in preparation for the performance of a lifetime. This journey will challenge you to never accept a sense of entitlement but to embrace a level of performance that will take you to greater heights both personally and professionally. Performance Driven Thinking will serve as your personal coach to a life of personal and professional prosperity. This journey will take you to a feeling of embracing life in the winner’s circle. It will assist you in overcoming the simple challenges of everyday issues to existing at a level which will benefit those who choose to take it. The key to this journey will begin when you discover the desire to perform and will end up with you embracing the will to perform. Non-performance in your life is no longer an option. Your stage is set. You have had a lifetime to prepare. Performance Driven Thinking will be your ticket to your personal and professional performance of a lifetime. What’s stopping you? You were born to perform.
Performance-Driven Journal
Using the proven strategies in Performance Driven Thinking, authors David Hancock and Bobby Kipper take things a step further - one's own written field manual to build a performance-driven life.
Dynamic Modeling, Predictive Control and Performance Monitoring
Author: Biao Huang
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-04-11
A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.