01883nas a2200229   4500008004100000020001400041245008700055210006900142260000600211300001400217490000700231520113700238653002301375653002401398653003101422653003001453653002701483100002701510700002601537700001901563856007101582       2016                        eng d  a0960-148100aWind speed forecasting for wind farms: A method based on support vector regression0 aWind speed forecasting for wind farms A method based on support   c1  a790–8090 v853 aIn this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, México, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5–23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.10aGenetic algorithms10aNon-linear analysis10aPhase space reconstruction10aSupport vector regression10aWind speed forecasting1 aSantamaría-Bonfil, G.1 aReyes-Ballesteros, A.1 aGershenson, C.  uhttp://www.sciencedirect.com/science/article/pii/S096014811530101401115nas a2200193   4500008004100000022001400041245002600055210002600081300001100107490000700118520063900125653002200764653002200786653001400808653002200822100002300844700001800867856003600885       2015                        eng d  a1099-052600aWhen slower is faster0 aWhen slower is faster  a9–150 v213 aThe slower is faster (SIF) effect occurs when a system performs worse as its components try to do better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples, we generalize common features of the SIF effect and suggest possible future lines of research. {\copyright} 2015 Wiley Periodicals, Inc. Complexity 21: 9–15, 201510acascading effects10acollective motion10aEvolution10aphase transitions1 aGershenson, Carlos1 aHelbing, Dirk  uhttp://arxiv.org/abs/1506.0679602024nam a2200157   4500008004100000020002200041245005000063210004900113260002600162520156600188653002201754653001201776653002201788100002301810856003301833       2007                        eng d  a978-0-9831172-3-000aDesign and Control of Self-organizing Systems0 aDesign and Control of Selforganizing Systems  aMexicobCopIt Arxives3 aComplex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this book I pro- pose a methodology to aid engineers in the design and control of com- plex systems. This is based on the description of systems as self- organizing. Starting from the agent metaphor, the methodology pro- poses a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by ac- tively interacting among themselves. The main premise of the method- ology claims that reducing the "friction" of interactions between el- ements of a system will result in a higher "satisfaction" of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while prac- tical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for im- proving communication within self-organizing bureaucracies are ad- vanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are dis- cussed. Philosophical implications of the conceptual framework are also put forward.10aComplexity Theory10aPhysics10aSelf-organization1 aGershenson, Carlos  uhttp://tinyurl.com/DCSOS2007