We address the problem of robust, computationally-efficient design of biological experiments. Classical optimal experiment design methods have not been widely adopted in biological practice, in part because the resulting designs can be very brittle if the nominal parameter estimates for the model are poor, and in part because of computational constraints. We present a method for robust experime...

this paper addresses adaptive observer design problem for joint estimation of the states and unknown parameters for a class of nonlinear systems which satisfying one-sided lipschitz and quadratic inner bounded conditions. it’s shown that the stability of the proposed observer is related to finding solutions to a quadratic inequality consists of state and parameter errors. a coordinate transform...

In this paper, we study the effects of Hall and ion-slip currents on the steady magneto-micropolar of a viscous incompressible and electrically conducting fluid over a horizontal plate by taking in to account the viscous dissipation effects. By means of similarity solutions, deviation of fundamental equations on the assumption of small magnetic Reynolds number are solved numerically by using qu...

Differential evolution (DE) is a powerful algorithm to find an optimal solution in real world problems. Nevertheless, the binomial crossover parameter is an important issue for the success of the algorithm. The proper selection of the binomial crossover parameter depends on the problem at hand. In this work, the effect of the binomial crossover in the DE/Rand/1/bin, DE/Best/1/Bin and DE/Current...

Conventional techniques for optimal experimental design are based on local sensitivity analysis to quantify parameter effects on the output. However, one of the key challenges for experimental design is that the local sensitivity is dependent on the unknown parameter values for a nonlinear model. This problem can be addressed if the sensitivity matrix, used in experimental design, could be comp...

Optimal Multi-parameter Auction Design

Often in the early stages of the engineering design process, a decision maker lacks the information needed to represent uncertainty in the input parameters of a performance model. In one particular form of severely deficient information, a nominal estimate is available for an input parameter, but the amount of discrepancy between that estimate and the parameter’s true value, as well as the impl...

An extension to the Dual Response approach to Robust Parameter Design for the case of multiple responses is proposed. The methodology provides unbiased estimates of the process covariance matrix and of the vector of expected values using parameter estimates from a multivariate regression fit. There are no restrictions on the types of experimental designs that can be used, apart from their abili...