A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. 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.

Differential-Evolution-Based Optimization of the Dynamic Response for Parallel Operation of Inverters With No Controller Interconnection

In this paper, the use of differential evolution ( DE), a global search technique inspired by evolutionary theory, to find the parameters that are required to achieve optimum dynamic response of parallel operation of inverters with no interconnection among the controllers is proposed. Basically, in order to reach such a goal, the system is modeled in a certain way that the slopes of P-omega and Q-V curves are the parameters to be tuned. Such parameters, when properly tuned, result in system's eigenvalues located in positions that assure the system's stability and oscillation-free dynamic response with minimum settling time. This paper describes the modeling approach and provides an overview of the motivation for the optimization and a description of the DE technique. Simulation and experimental results are also presented, and they show the viability of the proposed method.

Optimization Strategies Based on Sequential Quadratic Programming Applied for a Fermentation Process for Butanol Production

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Short and long period optimization of drug doses in the treatment of AIDS

Técnicas de otimização numérica são úteis na solução de problemas de determinação da melhor entrada para sistemas descritos por modelos matemáticos e cujos objetivos podem ser expressos de uma maneira quantitativa. Este trabalho aborda o problema de otimizar as dosagens dos medicamentos no tratamento da AIDS em termos de um balanço entre a resposta terapêutica e os efeitos colaterais. Um modelo matemático para descrever a dinâmica do vírus HIV e células CD4 é utilizado para calcular a dosagem ótima do medicamento no tratamento a curto prazo de pacientes com AIDS por um método de otimização direta utilizando uma função custo do tipo Bolza. Os parâmetros do modelo foram ajustados com dados reais obtidos da literatura. Com o objetivo de simplificar os procedimentos numéricos, a lei de controle foi expressa em termos de uma expansão em séries que, após truncamento, permite obter controles sub-ótimos. Quando os pacientes atingem um estado clínico satisfatório, a técnica do Regulador Linear Quadrático (RLQ) é utilizada para determinar a dosagem permanente de longo período para os medicamentos. As dosagens calculadas utilizando a técnica RLQ , tendem a ser menores do que a equivalente terapia de dose constante em termos do expressivo aumento na contagem das células T+ CD4 e da redução da densidade de vírus livre durante um intervalo fixo de tempo.

The use of simulation for forecasting the number of docks necessary in an expedition department

The simulation is a very powerful tool to develop more efficient systems, hence it is been widely used with the goal of productivity improvement. Its results, if compared with other methods, are not always optimum; however, if the experiment is rightly elaborated, its results will represent the real situation, enabling its use with a good level of reliability. This work used the simulation (through the ProModel (R) software) in order to study, understand, model and improve the expenditure system of an enterprise, with a premise of keeping the production-delivery flow considering quick, controlled and reliable conditions.

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.

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.

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.

Power flow optimization for grid connected inverter using evolutionary algorithm and additional control loop

In this work it is proposed to validate an evolutionary tuning algorithm in plants composed by a grid connected inverter. The optimization aims the tuning of the slopes of P-Ω and Q-V curves so that the system is stable, damped and minimum settling time. Simulation and experimental results are presented to prove the feasibility of the proposed approach. However, experimental results demonstrate a compromising effect of grid frequency oscillations in the active power transferring. In addition, it was proposed an additional loop to compensate this effect ensuring a constant active power flow. © 2011 IEEE.

Self-optimization of dense wireless sensor networks based on simulated annealing

Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.

Development of a thermoeconomic methodology for the optimization of biodiesel production - Part I: Biodiesel plant and thermoeconomic functional diagram

This work developed a methodology that uses the thermoeconomic functional diagram applied for allocating the cost of products produced by a biodiesel plant. The first part of this work discusses some definitions of exergy and thermoeconomy, with a detailed description of the biodiesel plant studied, identification of the system functions through Physical Diagram, calculation of the irreversibilities of the plant, construction of the Thermoeconomic Functional Diagram and determination of the expressions for the plant's exergetic functions. In order to calculate the exergetic increments and the physical exergy of certain flows in each step, the Chemical Engineering Simulation Software HYSYS 3.2 was used. The equipments that have the highest irreversibilities in the plant were identified after the exergy calculation. It was also found that the lowest irreversibility in the system refers to the process with a molar ratio of 6:1 and a reaction temperature of 60 °C in the transesterification process. In the second part of this work (Part II), it was calculated the thermoeconomic cost of producing biodiesel and related products, including the costs of carbon credits for the CO2 that is not released into the atmosphere, when a percentage of biodiesel is added to the petroleum diesel used by Brazil's internal diesel fleet (case study). © 2013 Elsevier Ltd. All rights reserved.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

A NS-2 simulation model for DNP3 protocol over IEEE 802.15.4 wireless protocol toward low cost simulation of Smart Grid applications

Faced with an imminent restructuring of the electric power system, over the past few years many countries have invested in a new paradigm known as Smart Grid. This paradigm targets optimization and automation of electric power network, using advanced information and communication technologies. Among the main communication protocols for Smart Grids we have the DNP3 protocol, which provides secure data transmission with moderate rates. The IEEE 802.15.4 is another communication protocol also widely used in Smart Grid, especially in the so-called Home Area Network (HAN). Thus, many applications of Smart Grid depends on the interaction of these two protocols. This paper proposes modeling, in the traditional network simulator NS-2, the integration of DNP3 protocol and the IEEE 802.15.4 wireless standard for low cost simulations of Smart Grid applications.

Architectures, stability and optimization for clock distribution networks

Synchronous telecommunication networks, distributed control systems and integrated circuits have its accuracy of operation dependent on the existence of a reliable time basis signal extracted from the line data stream and acquirable to each node. In this sense, the existence of a sub-network (inside the main network) dedicated to the distribution of the clock signals is crucially important. There are different solutions for the architecture of the time distribution sub-network and choosing one of them depends on cost, precision, reliability and operational security. In this work we expose: (i) the possible time distribution networks and their usual topologies and arrangements. (ii) How parameters of the network nodes can affect the reachability and stability of the synchronous state of a network. (iii) Optimizations methods for synchronous networks which can provide low cost architectures with operational precision, reliability and security. (C) 2011 Elsevier B. V. All rights reserved.

Designing piezoresistive plate-based sensors with distribution of piezoresistive material using topology optimization

Piezoresistive sensors are commonly made of a piezoresistive membrane attached to a flexible substrate, a plate. They have been widely studied and used in several applications. It has been found that the size, position and geometry of the piezoresistive membrane may affect the performance of the sensors. Based on this remark, in this work, a topology optimization methodology for the design of piezoresistive plate-based sensors, for which both the piezoresistive membrane and the flexible substrate disposition can be optimized, is evaluated. Perfect coupling conditions between the substrate and the membrane based on the `layerwise' theory for laminated plates, and a material model for the piezoresistive membrane based on the solid isotropic material with penalization model, are employed. The design goal is to obtain the configuration of material that maximizes the sensor sensitivity to external loading, as well as the stiffness of the sensor to particular loads, which depend on the case (application) studied. The proposed approach is evaluated by studying two distinct examples: the optimization of an atomic force microscope probe and a pressure sensor. The results suggest that the performance of the sensors can be improved by using the proposed approach.