Homepage     About Us     Contact   
  An innovator in modeling software and services
   Do the Polynomial Puzzle!
    TaylorFit Software Product Model Development Services Customer Applications   
Client Case Studies
  Primer on Modeling
  Our Products
  Other Applications
  MPR for Time Series Analysis
  Data Types Needed for MPR
  Example Applications
  Down Load Users' Manual FREE
  Logical Capabilities of MPR
  Other Modeling Methods

Helicopter performance for the U.S. Navy

The U.S. Navy had prepared extensive tables that allow computation of the lift produced by a helicopter rotor. There are as many as five variables that need to be taken into account:

  • Rotor speed
  • Rotor pitch
  • Rotor tilt
  • Air temperature
  • Air pressure

The Navy wanted to develop a software program to facilitate this calculation, but implementing a table lookup produced a large and complex program, plus it did not allow for analysis of the relationships among these variables.

The solution was to use MPR to generate a single equation that correlated the effect of all of these variables on lift. The application of MPR to this problem was described as "very successful."

Modeling and control of advanced life support systems for travel to Mars - NASA

NASA funded development of models to help with the control of advanced life support systems for possible future use on Mars. The models were used to predict gas exchange from crops in simulated Mars colony greenhouses.

Water Filtration Plant Performance

The New Jersey Department of Environmental Protection funded a project to develop models to predict the performance of water treatment filters. The models were used to predict turbidity that escapes the filter, based on measurements of particle size distribution entering the filter.

Demand forecasting for a natural gas provider in Europe

The energy company Blugas, SpA. in Mantua, Italy used MPR to develop models for natural gas demand forecasting. They needed to estimate the amount of gas they would need to provide to their customers the next day so they could optimize the amounts they would have to buy and store. The predictors included the forecast temperature, the gas demand on the current day, and day of the week.