Global optimization approach for the H2-norm model reduction problem

A branch and bound algorithm is proposed to solve the H2-norm model reduction problem for continuous-time linear systems, with conditions assuring convergence to the global optimum in finite time. The lower and upper bounds used in the optimization procedure are obtained through Linear Matrix Inequalities formulations. Examples illustrate the results.

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Optimization of the mechanical properties of low-carbon steels by formation of a multiphase microstructure

In this work five methods of heat treatments are investigated in order to obtained convenient volume fractions of ferrite, bainite, martensite and retained austenite, starting with a low carbon steel and seeking the distinction of the phases, through optical microscopy. Specific chemical etching is improved. The results in tensile and fatigue tests were accomplished and the results were related with the microstructural parameters. The results show that the mechanical properties are closely related with the phases, grains size and the phases morphology. Copyright © 2001 Society of Automotive Engineers, Inc.

Bound constrained smooth optimization for solving variational inequalities and related problems

Variational inequalities and related problems may be solved via smooth bound constrained optimization. A comprehensive discussion of the important features involved with this strategy is presented. Complementarity problems and mathematical programming problems with equilibrium constraints are included in this report. Numerical experiments are commented. Conclusions and directions of future research are indicated.

Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.

Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

Optimization of neutral hydrolysis reaction of post-consumer PET for chemical recycling

The neutral hydrolysis reaction of post-consumer poly(ethylene terephthalate) in solid state was studied through the reaction of the polymer with water at the molar ratio 1:91 with autogenous pressure. Two sizes of post-consumer PET flakes and temperatures of 135 °C, 170°C and 205°C with pressures of 4.0 atm, 7.5 atm and 13.5 atm, respectively, were considered. With reaction time equal to 6h, the method reached 99% depolymerization at 205°C, 8.2% at 170 °C and 1.7% at 135°C. The reaction extension was measured by separating the terephthalic acid formed in the process and calculating by gravimetry how much material could still be reacted. Through the viscosimetry of diluted, solutions and the counting of carboxylic end groups in the remaining material from the gravimetric assay, it was possible to suggest that the reaction occurs randomly and in the whole volume of the polymeric particle and not solely on the surface. The terephthalic acid obtained and then purified was characterized by elemental analysis, magnetic nuclear resonance, size and panicle size distribution and spectrophotometry in the visible spectrum, and it was similar to the petrochemical equivalent, with purity recorded in carbon base equal to 99.9%.

Optimization of xylanase biosynthesis by Aspergillus japonicus isolated from a Caatinga area in the Brazilian state of Bahia

The objective of this research was to investigate the potential of xylanase production by Aspergillus japonicus and to determine the effects of cultivation conditions in the process, aiming toward optimization of enzyme production. The best temperature, as well as the best carbon source, for biomass production was determined through an automated turbidimetric method (Bioscreen-C). The enzyme activity of this fungus was separately evaluated in two solid substrates (wheat and soybean bran) and in Vogel medium, adding other carbon sources. Temperature effects, cultivation time, and spore concentrations were also tested. The best temperature for enzyme and biomass production was 25°C; however, the best carbon source for growth (determined by the Bioscreen C) did not turn out to be a good inducer of xylanase production. Maximum xylanase activity was achieved when the fungus was cultivated in wheat bran (without the addition of any other carbon source) using a spore concentration of 1 × 107 spores/mL (25°C, pH 5.0, 120 h). A. japonicus is a good xylanase producer under the conditions presented in these assays. © 2006 Academic Journals.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

RAPD optimization for genetic studies with Citrulus lanatus and Sesamum indicum genotypes

The Random Amplified Polymorphic DNA (RAPD) technique is powerful for DNA polymorphism determinations and is widely used in research involving different organisms, but it is known that RAPD can be affected by many factors that may result in false positive bands and non-reproducible assays. In this study, we analyzed the effect of several factors such as DNA template, primer and Taq DNA polymerase concentrations to optimize and standardize the RAPD technique for further genetic studies with Citrulus lanattus and Sesamum indicum L. The best combination of DNA, Taq DNA polymerase enzyme and primer concentrations in RAPD amplification procedures for sesame and watermelon genotypes was established.

Global optimization for the ℋ∞-norm model reduction problem

A branch and bound algorithm is proposed to solve the [image omitted]-norm model reduction problem for continuous and discrete-time linear systems, with convergence to the global optimum in a finite time. The lower and upper bounds in the optimization procedure are described by linear matrix inequalities (LMI). Also proposed are two methods with which to reduce the convergence time of the branch and bound algorithm: the first one uses the Hankel singular values as a sufficient condition to stop the algorithm, providing to the method a fast convergence to the global optimum. The second one assumes that the reduced model is in the controllable or observable canonical form. The [image omitted]-norm of the error between the original model and the reduced model is considered. Examples illustrate the application of the proposed method.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.

A note on KKT-invexity in nonsmooth continuous-time optimization

We introduce the notion of KKT-inverity for nonsmooth continuous-time nonlinear optimization problems and prove that this notion is a necessary and sufficient condition for every KKT solution to be a global optimal solution.

Optimization of a novel nitric oxide sensor using a latex rubber matrix

This study present a novel NO sensor made of a spin trap (iron(II)-diethyldithiocarbamate complex, FeDETC) incorporated in a latex rubber matrix and works as a trap for NO, which is detectable by Electron Paramagnetic Resonance (EPR). We explored the optimization of our sensors changing systematically two fabrication parameters: the latex rubber matrix temperature of polymerization and FeDETC concentration inside the matrix. The sensor was prepared in four different temperatures: 4, 10, 20 and 40°C. The FeDETC concentration was also varied from 0.975 to 14.8 mM. We observed a variation of the EPR signals from the sensors prepared at different conditions. We found a high stability of the EPR response from our sensor, 40 days at RT. The best sensor was made with a latex rubber matrix polymerized at 10°C and with a FeDETC concentration of 14.8 mM. In vivo tests show good biocompatibility of our sensor. © 2007 Asian Network for Scientific Information.