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This paper describes a model of the immunologic response of the human immunodeficiency virus (HIV) in individuals. It then illustrates how a Receding Horizon Control (RHC) methodology can be used to drive the system to a stable equilibrium in which a strong immune response controls the viral load in the absence of drug treatment. We also illustrate how this feedback methodology can overcome unplanned treatment interruptions, inaccurate or incomplete data and imperfect model specification. We consider how ideas from stochastic estimation can be used in conjunction with RHC to create a robust treatment methodology. We then consider the performance of this methodology over random simulations of the previously considered clinical conditions.

This paper analyzes the ability of a neural network model to predict the outcome of NFL games. This model uses only readily available statistics, such as passing yards, rushing yards, fumbles lost, and scoring. A key component of this model is the use of statistical differentials to compare teams. For example, the offensive passing yards gained by one team are compared to the defensive passing yards allowed by an opposing team to create a data set of expected values for a given matchup. By using principal component analysis and derivative based analysis, we determined which statistics influence our model the most. We assessed the performance of the model by comparing its performance to that of published prediction algorithms and the Las Vegas oddsmakers over multiple seasons. Two novel aspects of this work include the use of multiple committees of machines for prediction and the use of our model to simulate virtual round-robin tournaments to establish an objective ranking of the teams.

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Optimal design of high-speed valve trains requires the use of an accurate analytical model. While the governing differential equations are important, the coefficients (or parameters) used in these equations are equally as important. Since many of the parameters used in valve train models are difficult to measure directly, parameter identification based on experimental data is required to assure model accuracy.This paper addresses the parameter identification problem for a valve train model, formulating a scalar cost function which represents the difference in measured and predicted system response. Minimizaton of this cost function yields the 10 unknown system parameters. As the cost function has many local minima, a global optimization scheme must be employed. An implicit filtering algorithm is implemented which applies a scale reduction scheme in conjunction with a gradient projection algorithm to avoid becomming trapped in local minima and thus produces near global minima of the cost function. The implicit filtering algorithm has several tuning parameters which allows its adaptation to many problems. For this problem, implicit filtering proved to be 2 to 4 times more efficient than the adaptive random search method previously employed.

This paper considers the problem of designing electron guns using computer optimization techniques. Several different design parameters are manipulated while considering multiple design criteria including beam and gun properties. The optimization routines are described. Examples of guns designed using these techniques are presented. Future research is also described.

Traveling wave tubes (TWT's) are vacuum devices invented in the early 1940's for amplification of radio frequency (RF) power.These devices are critical for radar, communications and electronic warfare missions in the military, as well as in commercial applications. The physics-based design and simulation code, CHRISTINE-1D, was used in the past to explore different TWT circuit designs and to automate the process of parameter estimation. However, the current capability of CHRISTINE-1D allows optimization of only helix TWT designs, and includes a limited number of optimization goal functions. In addition, the current optimizer in CHRISTINE-1D employs a modified steepest descent method to carry out the optimization process. The objectives of our work are three-fold: (i) to investigate optimization techniques that may be better suited for this problem (for example, simplex type methods such as Nelder-Mead and DIRECT), (ii) allow optimization of non-helix TWTs, (iii) and implement new optimization goal functions. Finally, to show the feasibility of our approach, we apply our optimization algorithms to the problem of designing a folded waveguide slowwave circuit.

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