Choice Of Objective Function Research Articles

Optimal operation of an embedded-piping floor heating system with control input constraints

Dynamic modelling and optimal control of an embedded-piping floor heating system is explored. The physical system consists of a single zone with serpentine tubes embedded in the floor slab. A gas-fired boiler supplies hot water in the tubes. The input energy to the boiler and the mass flow rate of hot water circulating in the tubes is modulated. A numerical solution to the 24-hour optimal control problem, taking into consideration the capacity constraints of the floor heating system, is found. Results show that, by proper choice of objective function, the energy input to the boiler can be minimized while maintaining good zone temperature control. The implementation of the optimal trajectories using PI controllers is also shown.

Hints.

The systematic use of hints in the learning-from-examples paradigm is the subject of this review. Hints are the properties of the target function that are known to use independently of the training examples. The use of hints is tantamount to combining rules and data in learning, and is compatible with different learning models, optimization techniques, and regularization techniques. The hints are represented to the learning process by virtual examples, and the training examples of the target function are treated on equal footing with the rest of the hints. A balance is achieved between the information provided by the different hints through the choice of objective functions and learning schedules. The Adaptive Minimization algorithm achieves this balance by relating the performance on each hint to the overall performance. The application of hints in forecasting the very noisy foreign-exchange markets is illustrated. On the theoretical side, the information value of hints is contrasted to the complexity value and related to the VC dimension.

Reservoir Operation: Choice of Objective Functions

An efficient algorithm for the real‐time monthly operation of a multipurpose reservoir is presented. The model is a combination of linear programming (used for month‐by‐month optimization) and dynamic programming (used for annual optimization). The use of parametric linear programming, minimum required beginning‐of‐month storages, and an iterative solution procedure result in low computer time and computer storage requirements. Low computer storage requirements allow the model to be run on minicomputers. Water and energy maximization, water and energy maximization with flood control considerations, and water and energy maximization for peak demand months are considered. Thus, the model provides the reservoir operator with different choices for annual optimization. The model is applied to Folsom reservoir of the California Central Valley Project and the results are discussed.

A linear programming approach to time-term analysis

abstractA linear programming approach to time-term analysis of seismic refraction data, one that is more adaptable than previous methods, has been developed. The travel-time equations are constraints to a choice of objective functions, which produce different models that fit the data. As examples: a “best-fit” model can be obtained by minimizing the L1 norm of the misfit errors; by maximizing and minimizing the sum of the time terms, the deepest and shallowest models can be derived. In addition, the method can be adapted to allow for refractor anisotropy without any assumptions on the form of the anisotropy.Examples with sets of theoretical data illustrate the different ways in which the technique can be used. A set of travel times for the Pn phase from the northeast Pacific, previously interpreted to document compressional wave velocity anisotropy, exemplifies the application of the method.

Optimization Method for Railroad Curve Alinement

Realignment of existing railroad curves is cast in terms of a quadratic optimization problem and a method of minimizing the objective function is developed. Geometry relating track curvature and movement are presented, and quadratic objective functions which measure curve smoothness are derived. The objective function is minimized using a modified form of Cauchy's method, and integer constraints are applied. Results of the procedure are track movements required to move the existing alignment to a new smooth alignment. Computer results are presented for a typical curve. The method works well in finding smooth alignments and is not very sensitive on choice of objective function. The method presented shows much improvement over the current, hand trial and error method employed by railroads which is called Stringlining.

A program library for computing the parameters of linear compartment models in pharmacokinetics

A program library is presented with which explicit solutions of linear compartment models can be fitted to pharmacokinetic experimental data. Use is made of the fact that transport constants can be calculated from the numerical solution of such a problem without ambiguity. The non-linear regression is carried out using the Powell-method. A report of our experience with, e.g. choice of objective function, lag-time, accuracy of the numerical solution is included.

SIMULATION RESULTS ON THE EVALUATION OF ECONOMIC POLICIES

There has recently been a trend toward sophistication in the methods for finding optimal controls of economic systems. Criticism has been voiced about the practical usefulness of such approaches. After giving some attention to the choice of objective function, the present paper considers simple, easily implementable proportional feedback policies and investigates how far they are from the „true” optimum. The effect of lags in the feedback control is also considered. The policy simulation experiments are carried out on Pindyck's well known 1973 US econometric model using the TROLL computer system, incorporating an optimal control algorithm with an essentially quadratic welfare cost function.