Artificial neural networks (ANN) and response surface methodology (RSM) were used to build a model to describe the effects of four independent variables (moisture content, concentrations of glucose, ammonium nitrate and methionine) on the yield of cephalosporin C (CPC) from Acremonium chrysogenum under solid state fermentation. The respective uses of RSM and ANN were found to be effective in lo...

Resource Space Model (RSM) is a semantic model to manage and share heterogeneous resources on the Internet. This paper focuses on the general architecture, physical implementation and application of RSM. The RSM system adopts Client/Server mode for users to utilize resource spaces through the Internet. The resource spaces are designed based on domain knowledge and stored as XML files in the ser...

Device robust-design is inherently a multiple-objective optimization problem. Using design of experiments (DoE) combined with response surface methodology (RSM) can satisfy the great incentive to reduce the number of TCAD simulations that need to be performed. However, the errors of RSM models may large enough to diminish the validity of the results for some nonlinear problems. To find the feas...

In this paper, we present a new mutation operator, Hybrid Mutation (HPRM), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The Hybrid Mutation operator constructs an offspring from a pair of parents by hybridizing two mutation operators, PSM and RSM. The efficiency of the HPRM is compared as against some existing mutation operators; namely,...

R surface methodology (RSM) is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is popular in practice, its current appl...

Biofiltration is emerging as a promising cost effective technique for the Volatile Organic Compounds (VOCs) removal from industrial waste gases. In the present investigation a comparative modeling study has been carried out using Radial Basis Function Neural Network (RBFN) and Response Surface Methodology (RSM) to predict and optimize the performance of a biofilter system treating toluene (a mo...

Response Surface Methodology (RSM) is a popular class of methods used to build empirical models through statistical means. Rarely in the literature is the RSM applied to model characteristics of Evolutionary Algorithm (EA) based techniques used to solve dynamic optimization problems. This paper presents RSM as a modelling tool of some characteristics of a memory and EA-based approach referred t...

The response surface methodology (RSM), which typically uses quadratic polynomials, is predominantly used for metamodeling in crashworthiness optimization because of the high computational cost of vehicle crash simulations. Research shows, however, that RSM may not be suitable for modeling highly nonlinear responses that can often be found in impact related problems, especially when using limit...