Explaining commodity prices through asymmetric oil shocks: evidence from nonlinear models

Linkages between oil and 25 other commodity prices are examined using annual data for 1900 to 2011. We identify long-run relationships using both linear and nonlinear ARDL models and capture short-run causalities through asymmetric Granger causality tests. Nonlinearity can't be rejected for the relationship between oil and most other commodity prices. Long-run positive impacts of oil price increases are found for 20 commodities and short-run negative impacts for 13 commodity prices. Oil prices don't have much impact on beverage or cereal prices once endogeneity is accounted for, but they have substantial impact on metal prices.

Nonlinear identification of pneumatic servo-drive

Three nonlinear approaches to model the nonlinear pneumatic servo- drive are presented. The three nonlinear approaches are: (1) the multi input-single output (MISO) approach, which describes the single input-single output (SISO) nonlinear plant using a MISO linear representation which allows replacement of the nonlinear analysis by a linear one without approximation, and is studied in both time and frequency domains; (2) piecewise linearization, which systematically replaces, using artificial neural network, the nonlinear surface representing the plant in the hyper input-output space by a number of linear planes that are continuous over the boundaries between them; and (3) Adaptive Neuro-Fuzzy Inference System (ANFIS), in which the fuzzy rules are placed in a neural network structure, and which consequently utilizes neural networks learning rules to systematically tune the nonlinear fuzzy model. The superiority of these nonlinear models over the best model that can be developed using linear identification techniques is shown.

The exchange rate disconnect puzzle: a resolution?

Early empirical studies of exchange rate determinants demonstrated that fundamentals-based monetary models were unable to outperform the benchmark random walk model in out-of-sample forecasts while later papers found evidence in favor of long-run exchange rate predictability. More recent theoretical works have adopted a microeconomic structure; a utility-based new open economy macroeconomic framework and a rational expectations present value model. Some recent empirical work argues that if the models are adjusted for parameter instability, it is a good predictor of nominal exchange rates while others use aggregate idiosyncratic volatility to generate good predictions. This latest research supports the idea that fundamental economic variables are likely to influence exchange rates especially in the long run and further that the emphasis should change to the economic value or utility based value to assess these macroeconomic models.

Robust control for power sharing in microgrids with low-inertia wind and PV generators

This paper presents a robust control design scheme for a multidistributed energy resource (DER) microgrid for power sharing in both interconnected and islanded modes. The scheme is proposed for micgrogrids consisting of photovoltaic (PV) units and wind turbine driven doubly fed induction generators (DFIGs). A battery is integrated with each of the wind and solar DER units. The control scheme has two levels: 1) one centralized multi-input–multi-output robust controller for regulating the set reference active and reactive powers and 2) local real and reactive power droop con-trollers, one on each DER unit. The robust control scheme utilizes multivariable H1 control to design controllers that are robust to the changes in the network and system nonlinearities. The effectiveness of the proposed controller is demonstrated through large-distur-bance simulations, with complete nonlinear models, on a test micro-grid. It is found that the power sharing controllers provide excellent performance against large disturbances and load variations during islanding transients and interconnected operation.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.

Nonlinear large deformation dynamic analysis of electroactive polymer actuators

Electroactive polymers have attracted considerable attention in recent years due to their sensing and actuating properties which make them a material of choice for a wide range of applications including sensors, biomimetic robots, and biomedical micro devices. This paper presents an effective modeling strategy for nonlinear large deformation (small strains and moderate rotations) dynamic analysis of polymer actuators. Considering that the complicated electro-chemo-mechanical dynamics of these actuators is a drawback for their application in functional devices, establishing a mathematical model which can effectively predict the actuator's dynamic behavior can be of paramount importance. To effectively predict the actuator's dynamic behavior, a comprehensive mathematical model is proposed correlating the input voltage and the output bending displacement of polymer actuators. The proposed model, which is based on the rigid finite element (RFE) method, consists of two parts, namely electrical and mechanical models. The former is comprised of a ladder network of discrete resistive-capacitive components similar to the network used to model transmission lines, while the latter describes the actuator as a system of rigid links connected by spring-damping elements (sdes). Both electrical and mechanical components are validated through experimental results.

Hybrid controller with the combination of FLC and neural network-based IMC for nonlinear processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.

Robust adaptive backstepping excitation controller design for simple power system models with external disturbances

This paper presents a nonlinear robust adaptive excitation controller design for a simple power system model where a synchronous generator is connected to an infinite bus. The proposed controller is designed to obtain the adaption laws for estimating critical parameters of synchronous generators which are considered as unknown while providing the robustness against the bounded external disturbances. The convergence of different physical quantities of a single machine infinite bus (SMIB) system, with the proposed control scheme, is ensured through the negative definiteness of the derivative of Lyapunov functions. The effects of external disturbances are considered during formulation of Lyapunov function and thus, the proposed excitation controller can ensure the stability of the SMIB system under the variation of critical parameters as well as external disturbances including noises. Finally, the performance of the proposed scheme is investigated with the inclusion of external disturbances in the SMIB system and its superiority is demonstrated through the comparison with an existing robust adaptive excitation controller. Simulation results show that the proposed scheme provides faster responses of physical quantities than the existing controller.

Chaotic synchronization of time-delay coupled Hindmarsh–Rose neurons via nonlinear control

Chaotic synchronization of two time-delay coupled Hindmarsh–Rose neurons via nonlinear control is investigated in this paper. Both the intrinsic slow current delay in a single Hindmarsh–Rose neuron and the coupling delay between the two neurons are considered. When there is no control, chaotic synchronization occurs for a limited range of the coupling strength and the time-delay values. To obtain complete chaotic synchronization irrespective of the time-delay or the coupling strength, we propose two nonlinear control schemes. The first uses adaptive control for chaotic synchronization of two electrically coupled delayed Hindmarsh–Rose neuron models. The second derives the sufficient conditions to ensure a complete synchronization between master and slave models through appropriate Lyapunov–Krasovskii functionals and the linear matrix inequality technique. Numerical simulations are carried out to show the effectiveness of the proposed methods.