The goal of this report is to provide a model accurately predicting whether a worker’s compensation policy will suffer a large loss. Several models were created using statistical analysis and the final logistic regression model predicting client policies at risk for large losses is provided. This model is a two-factor logistic regression model accurately predicted the large loss outputs of 87.5...

Model predictive control, MPC, is a model-based control philosophy that select control actions by on-line optimization of objective functions. Design methods based on MPC have found wide acceptance in industrial process control applications, and have been thoroughly studied by the academia. Most of the work so far have relied on linear models of different sophistication because of their advanta...

Most academic control schemes for MIMO systems assume all the control variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper s...

Often the goal of model selection is to choose a model for future prediction, and it is natural to measure the accuracy of a future prediction by squared error loss. Under the Bayesian approach, it is commonly perceived that the optimal predictive model is the model with highest posterior probability, but this is not necessarily the case. In this paper we show that, for selection among normal l...

A highly nonlinear system controlled by a linear model predictive controller (MPC) may not exhibit a satisfactory dynamic performance. This has led to the development of a number of nonlinear MPC (NMPC) approaches that permit the use of first principles-based nonlinear models. Such models can be accurate over a wide range of operating conditions, but may be difficult to develop for many industr...

We give a finite-sample analysis of predictive inference procedures after model selection in regression with random design. The analysis is focused on a statistically challenging scenario where the number of potentially important explanatory variables can be infinite, where no regularity conditions are imposed on unknown parameters, where the number of explanatory variables in a “good” model ca...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems. We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nominal setting.