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Response Surface Method (response + surface_method)
Selected AbstractsIntegrative optimization by RBF network and particle swarm optimizationELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 12 2009Satoshi Kitayama Abstract This paper presents a method for the integrative optimization system. Recently, many methods for global optimization have been proposed. The objective of these methods is to find a global minimum of nonconvex function. However, large numbers of function evaluations are required, in general. We utilize the response surface method to approximate function space to reduce the function evaluations. The response surface method is constructed from sampling points. The RBF Network, which is one of the neural networks, is utilized to approximate the function space. Then Particle Swarm Optimization (PSO) is applied to the response surface. The proposed system consists of three parts: (Part 1) generation of the sampling points, (Part 2) construction of response surface by RBF Network, (Part 3) optimization by PSO. By iterating these three parts, it is expected that the approximate global minimum of nonconvex function can be obtained with a small number of function evaluations. Through numerical examples, the effectiveness and validity are examined. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(12): 31,42, 2009; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecj.10187 [source] Approximation methods for reliability-based design optimization problemsGAMM - MITTEILUNGEN, Issue 2 2007Irfan Kaymaz Abstract Deterministic optimum designs are obtained without considering of uncertainties related to the problem parameters such as material parameters (yield stress, allowable stresses, moment capacities, etc.), external loadings, manufacturing errors, tolerances, cost functions, which could lead to unreliable designs, therefore several methods have been developed to treat uncertainties in engineering analysis and, more recently, to carry out design optimization with the additional requirement of reliability, which referred to as reliability-based design optimization. In this paper, two most common approaches for reliability-based design optimization are reviewed, one of which is reliability-index based approach and the other performancemeasure approach. Although both approaches can be used to evaluate the probabilistic constraint, their use can be prohibitive when the associated function evaluation required by the probabilistic constraint is expensive, especially for real engineering problems. Therefore, an adaptive response surface method is proposed by which the probabilistic constraint is replaced with a simple polynomial function, thus the computational time can be reduced significantly as presented in the example given in this paper. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Two-stage computing budget allocation approach for the response surface methodINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 6 2007J. Peng Abstract Response surface methodology (RSM) is one of the main statistical approaches to search for an input combination that optimizes the simulation output. In the early stages of RSM, an iterative steepest ascent search procedure is frequently used. In this paper, we attempt to improve this procedure by considering a more realistic case where there are computing budget constraints, and formulate a new computing budget allocation problem to look into the important issue of allocating computing budget to the design points in the local region of experimentation. We propose a two-stage computing budget allocation approach, which uses a limited budget to estimate the response surface in the first stage and then uses the rest of the budget to improve the lower bound of the estimated response at the center of the next design region in the second stage. Several numerical experiments are carried out to compare the two-stage approach with the regular factorial design, which allocates budget equally to each design point. The results show that our two-stage allocation outperforms the equal allocation, especially when the system noise is large. [source] Sensory Modeling of Coffee with a Fuzzy Neural NetworkJOURNAL OF FOOD SCIENCE, Issue 1 2002O. Tominaga ABSTRACT: Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty-two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup-tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development. [source] A kriging method for the solution of nonlinear programs with black-box functionsAICHE JOURNAL, Issue 8 2007Eddie Davis Abstract In this article, a new methodology is developed for the optimization of black-box systems lacking a closed-form mathematical description. To properly balance the computational cost of building the model against the probability of convergence to global optimum, a hybrid methodology is proposed. A kriging approach is first applied to provide information about the global behavior of the system considered, whereas a response surface method is considered close to the optimum to refine the set of candidate local optima and find the global optimum. The kriging predictor is a global model employing normally distributed basis functions, so both an expected sampling value and its variance are obtained for each test point. The presented work extends the capabilities of existing response surface techniques to address the refinement of optima located in regions described by convex asymmetrical feasible regions containing arbitrary linear and nonlinear constraints. The performance of the proposed algorithm is compared to previously developed stand-alone response surface techniques and its effectiveness is evaluated in terms of the number of function calls required, number of times the global optimum is found, and computational time. © 2007 American Institute of Chemical Engineers AIChE J, 2007 [source] Stimulatory Effect of Procaine on the Growth of Several Microalgae and CyanobacteriaJOURNAL OF PHARMACY AND PHARMACOLOGY: AN INTERNATI ONAL JOURNAL OF PHARMACEUTICAL SCIENCE, Issue 2 2000TAKAHIRO SUZUKI Procaine has been used to stimulate plant growth and it has been noted that it also promotes growth of microorganisms. The effect of procaine hydrochloride concentration on the growth rates of several species of microalgae and cyanobacteria was studied under both photoautotropic and heterotrophic growth conditions. Procaine hydrochloride was added to cultures at concentrations over the range 0.01,1000 mg L,1. A stimulating effect of procaine hydrochloride on photoautotrophic growth was observed for the cyanobacteria Anabaena cylindrica and Anabaena variabilis, and for the salt-tolerant green algae Dunaliella primolecta and Dunaliella parva. During active growth in batch culture an increase in growth rate (compared with control culture without procaine hydrochloride) of about 25% was observed at 0.1 mg L,1 of procaine hydrochloride for A. cylindrica. However, procaine hydrochloride was toxic at concentrations of > 10 mg L,1. Simultaneous administration of hydrolysis products of procaine, p -amino-benzoic acid and diethyl aminoethanol, in lieu of procaine hydrochloride, was as effective as procaine in stimulating growth of A. cylindrica. Heterotrophic growth of Chlorella ellipsoidea and Prototheca zopfii was not stimulated by procaine hydrochloride over the concentration range studied (0.1,10 mg L,1). The combined effects of procaine hydrochloride concentration and four other environmental factors (temperature, light intensity, CO2 concentration in the flushing gas and NaCl concentration) on growth rate of D. primolecta was modelled using both a neural network approach and a response surface method. These results indicate that procaine hydrochloride exerts different effects on the growth of microalgal and cyanobacterial cells as functions of dosage, species and culture conditions. [source] Design of optimal extrusion die for a range of different materialsPOLYMER ENGINEERING & SCIENCE, Issue 3 2009Nadhir Lebaal THE coat-hanger melt distributor is a device commonly used in the wire coating process. Its task is to distribute the melt around the conductor uniformly. It is quite common that materials and flow rates differ from what had been specified during the design procedure. This may lead to bad performance with materials of very different rheological properties from the design material. In this article, we present an optimal design approach to avoid this loss of performances. This approach involves coupling a three-dimensional finite element simulation software with an optimization strategy based on a response surface method. The objective is to determine a coat-hanger melt distributor geometry that ensures a homogeneous exit velocity distribution that will best accommodate for a different range of materials. A coat-hanger melt distributor with a manifold of constant width is designed, and a set of flow distribution measurements is established for two different materials. The results of numerical simulation are then validated by comparison with experimental measurements. The effect of material change is also investigated. POLYM. ENG. SCI., 2009. © 2008 Society of Plastics Engineers [source] | ||||||||||