Control of robots using radial basis function neural networks with dead‐zone

In this paper, we examine the control of robot manipulators utilizing a Radial Basis Function (RBF) neural network. We are able to remove the typical requirement of Persistence of Excitation (PE) for the desired trajectory by introducing an error minimizing dead‐zone in the learning dynamics of the neural network. The dead‐zone freezes the evolution of the RBF weights when the performance error is within a bounded region about the origin. This guarantees that the weights do not go unbounded even if the PE condition is not imposed. Utilizing protection ellipsoids we derive conditions on the feedback gain matrices that guarantee that the origin of the closed loop system is semi‐globally uniformly bounded. Simulations are provided illustrating the techniques. Copyright © 2011 John Wiley & Sons, Ltd.     

Evolution of Flavylium-Based Color Systems in Plants: What Physical Chemistry Can Tell Us

Anthocyanins are the basis of the color of angiosperms, 3-deoxyanthocyanins and sphagnorubin play the same role in mosses and ferns, and auronidins are responsible for the color in liverworts. In this study, the color system of cyanidin-3-O-glucoside (kuromanin) as a representative compound of simpler anthocyanins was fully characterized by stopped flow. This type of anthocyanin cannot confer significant color to plants without intra- or intermolecular interactions, complexation with metals or supramolecular structures as in Commelina communis. The anthocyanin’s color system was compared with those of 3-deoxyanthocyanins and riccionidin A, the aglycone of auronidins. The three systems follow the same sequence of chemical reactions, but the respective thermodynamics and kinetics are dramatically different.     

Initialization of nonlinear state-space models applied to the Wiener–Hammerstein benchmark

In this work a new initialization scheme for nonlinear state-space models is applied to the problem of identifying a Wiener–Hammerstein system on the basis of a set of real data. The proposed approach combines ideas from the statistical learning community with classic system identification methods. The results on the benchmark data are discussed and compared to the ones obtained by other related methods.     

First and Second Order SMO Algorithms for LS-SVM Classifiers

Least squares support vector machine (LS-SVM) classifiers have been traditionally trained with conjugate gradient algorithms. In this work, completing the study by Keerthi et al., we explore the applicability of the SMO algorithm for solving the LS-SVM problem, by comparing First Order and Second Order working set selections concentrating on the RBF kernel, which is the most usual choice in practice. It turns out that, considering all the range of possible values of the hyperparameters, Second Order working set selection is altogether more convenient than First Order. In any case, whichever the selection scheme is, the number of kernel operations performed by SMO appears to scale quadratically with the number of patterns. Moreover, asymptotic convergence to the optimum is proved and the rate of convergence is shown to be linear for both selections.     

Solving a rich vehicle routing and inventory problem using column generation

The livestock collection problem (LCP) is a rich vehicle routing problem (VRP) extended with inventory constraints. The LCP is a complex planning problem taken from the meat industry, and the goal is to construct a set of vehicle routes to collect animals from farms for slaughter at a slaughterhouse. Several constraints dealing with animal welfare are added, some of these lead to a loading problem where the vehicle capacity depends on the loading sequence. In addition, global constraints to handle production and inventory at the slaughterhouse are needed. This paper presents an exact solution method for the LCP, based on column generation, that solves much larger instances to optimality than what has been done before. The algorithm presented here also solves a richer model that is closer to the underlying real-world problem than previously published work on exact methods for this problem is based on.     

Profiting from negawatts: Reducing absolute consumption and emissions through a performance-based energy economy

Current energy and GHG emissions policies either focus directly on emissions or promote renewable production and the implementation of specific efficiency measures. Meanwhile, the fundamental structure of the energy market based on profits through energy throughput remains largely unchallenged. This policy oversight prevents the transition to an energy economy in which profits are based on energy services delivered at the lowest energy cost: a performance-based energy economy (PBEE). The PBEE applies the combined concepts of the performance economy and energy services to the energy sector. Energy Service Companies (ESCOs) are discussed as an example of PBEE practices. The implications for energy suppliers and consumers as well as the conditions for PBEE diffusion and consequences for technological change are also explored. The expected environmental, social and economic benefits are described. However, absolute consumption and emissions reductions may prove elusive due to the rebound effect. In order to forestall rebound-led increases, complementary policy measures likely to lead to absolute reductions are required.