{"id":22515,"date":"2026-07-16T13:30:12","date_gmt":"2026-07-16T11:30:12","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/use-cases\/performance-optimization\/"},"modified":"2026-07-16T13:47:03","modified_gmt":"2026-07-16T11:47:03","slug":"performance-optimization","status":"publish","type":"page","link":"https:\/\/www.neuraldesigner.com\/use-cases\/performance-optimization\/","title":{"rendered":"Performance optimization using machine learning"},"content":{"rendered":"
Tracking the behavior of a system allows us to predict its behavior and improve its performance.<\/p>\n

However, the performance of a process may depend on a large number of variables. Therefore, sophisticated methods and tools are needed to untangle all these factors effectively.<\/p>\n

This article explains how to use machine learning and Neural Designer<\/a>\u00a0to model engineering systems and optimize industrial processes.<\/p>\n

Contents<\/h3>\n
    \n
  1. Data set<\/a>.<\/li>\n
  2. Mathematical model<\/a>.<\/li>\n
  3. Response optimization<\/a>.<\/li>\n
  4. Conclusions<\/a>.<\/li>\n<\/ol>\n<\/section>\n
    \n

    Data set<\/h2>\n

    The data set<\/a> contains measurements from our system or process.<\/p>\n

    It comprises state, control, and performance variables<\/a>.<\/p>\n

    State variables<\/b> are those inputs<\/a>\u00a0that determine the system’s performance and are not actionable by the company’s technicians.<\/p>\n

    Some examples of state variables are:<\/p>\n