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The present work aims to explain how the nonlinear average model can be used in power electronic integration design as a behavioral model.

The nonlinear average model is used in power electronic integration design as a behavioral model, where it is applied to a voltage source inverter based on IGBTs. This model was chosen because it takes into account the nonlinearity of the power semiconductor components and the wiring circuit effects, which can be formalized by the virtual delay concept. In addition, the nonlinear average model cannot distinguish between slow and quick variables and this is an important feature of the model convergence.

The paper studies extensively the construction of the nonlinear average model algorithm theoretically. Detailed explanations of the application of this model to voltage source inverter design are provided. The study demonstrates how this model illustrates the effect of the nonlinearity of the power semiconductor components' characteristics on dynamic electrical quantities. It also predicts the effects due to wiring in the inverter circuit.

More simulations and experimental analysis are still necessary to improve the model's accuracy, by using other static characteristic approaches, and to validate the applicability of the model to different converter topologies.

The paper formulates a simple nonlinear average model algorithm, discussing each step. This model was described by VHDL‐AMS. On the one hand, it will assist theoretical and practical research on different topologies of power electronic converters, particularly in power integration systems design such as the integrated power electronics modules (IPEM). On the other hand, it will give designers a more precise behavioral model with a simpler design process.

The nonlinear average model used in power electronic integration design as behavioral model is a novel approach. This model reduces computational costs significantly, takes physical effects into account and is easy to implement.

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting accuracy. This paper aims to apply multiple attribute decision-making (MADM) methods to develop new combination forecasting methods.

Grey relational analysis (GRA) is applied to assess weights for individual constituents, and the Choquet fuzzy integral is employed to nonlinearly synthesize individual forecasts from single grey models, which are not required to follow any statistical property, into a composite forecast.

The empirical results indicate that the proposed method shows the superiority in mean accuracy over the other combination methods considered.

For tourism practitioners who have no experience of using grey prediction, the proposed methods can help them avoid the risk of forecasting failure arising from wrong selection of one single grey model. The experimental results demonstrated the high applicability of the proposed nonadditive combination method for tourism demand forecasting.

By treating both weight assessment and forecast combination as MADM problems in the tourism context, this research investigates the incorporation of MADM methods into combination forecasting by developing weighting schemes with GRA and nonadditive forecast combination with the fuzzy integral.

The purpose of this paper is to study the properties of the NGM (1,1,k) prediction model with multiplication transformation and reduce its modeling complexity.

The authors improved this model by putting forward a formula to solve its parameters, building an algorithm for optimizing the NGM (1,1,k) model in terms of the least modeling error and designing a key technology for the implementation of this algorithm. The optimized NGM (1,1,k) model is built accordingly. The parameter characteristics of the two models under multiple transformations and its effect of the simulation value and forecasting value are analyzed by studying the properties of multiple transformation of the two models.

The research finding shows that the modeling accuracies of the NGM (1,1,k) model and the optimized NGM (1,1,k) model are all in no relation to multiple transformations.

The above results imply that the data level can be reduced; the process of building the NGM (1,1,k) model and the optimized NGM (1,1,k) model can be simplified; but the simulative and predictive accuracy of the two models remain unchanged.

The paper succeeds in realising the properties of NGM (1,1,k) model and the optimized NGM (1,1,k) model by using the method of multiplication transformation, which is helpful for understanding the modeling mechanism and expanding the application range of the NGM (1,1,k) model.

This paper aims to propose a precise turbulence model for automobile aerodynamics simulation, which can predict flow separation and reattachment phenomena more accurately.

As the results of wake flow simulation with commonly used turbulence models are unsatisfactory, by introducing a nonlinear Reynolds stress term and combining the detached Eddy simulation (DES) model, this paper proposes a nonlinear-low-Reynolds number (LRN)/DES turbulence model. The turbulence model is verified in a backward-facing step case and applied in the flow field analysis of the Ahmed model. Several widely applied turbulence models are compared with the nonlinear-LRN/DES model and the experimental data of the above cases.

Compared with the experimental data and several turbulence models, the nonlinear-LRN/DES model gives better agreement with the experiment and can predict the automobile wake flow structures and aerodynamic characteristics more accurately.

The nonlinear-LRN/DES model proposed in this paper suffers from separation delays when simulating the separation flows above the rear slant of the Ahmed body. Therefore, more factors need to be considered to further improve the accuracy of the model.

This paper proposes a turbulence model that can more accurately simulate the wake flow field structure of automobiles, which is valuable for improving the calculation accuracy of the aerodynamic characteristics of automobiles.

Based on the nonlinear eddy viscosity method and the scale resolved simulation, a nonlinear-LRN/DES turbulence model including the nonlinear Reynolds stress terms for separation and reattachment prediction, as well as the wake vortex structure prediction is first proposed.

The purpose of this paper is to explore the dynamics of the relationships between sales and internal and external environmental drivers in a retail company using a systems perspective in order to support retail management decisions with nonlinear methods.

The research and results are presented in two parts: the collection and explorative analysis of the data; and discussion of the managerial implications following a systems perspective. The exploratory analysis is conducted using a statistical comparison of linear and nonlinear models of sales data from a retail company. The data, which comprise two data sets, come from 45 retail stores located in different regions of the USA.

Specifically, nonlinear models provided a better explanation of variation in retail activity (R²=46 per cent) than linear models (R²=16 per cent). In such a situation, the nonlinear analysis captures the influence of internal and external environmental drivers on retail sales.

With a limited variety of external and internal drivers, the exploratory analysis aims to describe a general situation in which retailers are managing activities in complex environments as opposed to reflect on a particular retail chain.

The systems perspective is used to interpret the managerial implications of the nonlinear analysis fits, particularly in cases where retail decision-makers are adapting, transforming and restructuring sources of competitive advantage in complex environments.

The paper provides an alternative perspective (the systemic one) of how retailers could interpret the relationships between internal and external variables in the dynamic environment of the retail chains with nonlinear models.

The purpose of this paper is to examine the determinants of average health expenditures for inpatients in China with national data for period 2002-2010 and regional data during 2005-2010.

The semi-parametric framework is established to identify the determinants of health expenditures with local-constant least squares (LCLS) and local-linear least squares (LLLS) techniques. The LCLS technique aims to identify correlative determinants among all considered variables, and LLLS technique aims to further distinguish linear decisive and nonlinear control variables among all correlative determinants.

First, root mean square error tends to decrease with irrelative variables smoothed out in regression model, validating the modelling reasonability of the semi-parametric approach. Second, the determinants of average health expenditures for inpatients exhibit considerable variation among regions despite the fact that governmental health expenditure, GDP per-capita, and urbanization do impact average health expenditures for inpatients to a certain extent. Third, both linear decisive and nonlinear control variables vary greatly with national, provincial, and regional data.

First, the illiteracy rate should be further reduced nationally. Second, urbanization development and the average treatment number of inpatients for each physician per day should be strictly controlled in region A and C, respectively, in order to control average health expenditure for inpatients.

First, the semi-parametric framework with LCLS and LLLS techniques allows for data structure-oriented model in regions rather than a uniform and definite model for underlying structure. Second, the research undertakes for the first time a comprehensive data analysis of the determinants of average health expenditures for inpatients with national and regional data in China.

The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power forecasting. For this purpose, a decomposed based series-parallel hybrid model (PKF-ARIMA-FMLP) is proposed which can model linear/nonlinear and certain/uncertain patterns in underlying data simultaneously.

To design the proposed model at first, underlying data are divided into two categories of linear and nonlinear patterns by the proposed Kalman filter (PKF) technique. Then, the linear patterns are modeled by the linear-fuzzy nonlinear series (LLFN) hybrid models to detect linearity/nonlinearity and certainty/uncertainty in underlying data simultaneously. This step is also repeated for nonlinear decomposed patterns. Therefore, the nonlinear patterns are modeled by the linear-fuzzy nonlinear series (NLFN) hybrid models. Finally, the weight of each component (e.g. KF, LLFN and NLFN) is calculated by the least square algorithm, and then the results are combined in a parallel structure. Then the linear and nonlinear patterns are modeled with the lowest cost and the highest accuracy.

The effectiveness and predictive capability of the proposed model are examined and compared with its components, based models, single models, series component combination based hybrid models, parallel component combination based hybrid models and decomposed-based single model. Numerical results show that the proposed linear-nonlinear data preprocessing-based hybrid models have been able to improve the performance of single, hybrid and single decomposed based prediction methods by approximately 66.29%, 52.10% and 38.13% for predicting wind power time series in the test data, respectively.

The combination of single linear and nonlinear models has expanded due to the theory of the existence of linear and nonlinear patterns simultaneously in real-world data. The main idea of the linear and nonlinear hybridization method is to combine the benefits of these models to identify the linear and nonlinear patterns in the data in series, parallel or series-parallel based models by reducing the limitations of the single model that leads to higher accuracy, more comprehensiveness and less risky predictions. Although the literature shows that the combination of linear and nonlinear models can improve the prediction results by detecting most of the linear and nonlinear patterns in underlying data, the investigation of linear and nonlinear patterns before entering linear and nonlinear models can improve the performance, which in no paper this separation of patterns into two classes of linear and nonlinear is considered. So by this new data preprocessing based method, the modeling error can be reduced and higher accuracy can be achieved at a lower cost.

This paper aims to predict turbulent flow and heat transfer through different channels with periodic dimple/protrusion walls. More specifically, the performance of various low-Re k-ε turbulence models in prediction of local heat transfer coefficient is evaluated.

Three low-Re number k-ε turbulence models (the zonal k-ε, the linear k-ε and the nonlinear k-ε) are used. Computations are performed for three geometries, namely, a channel with a single dimpled wall, a channel with double dimpled walls and a channel with a single dimple/protrusion wall. The predictions are obtained using an in house finite volume code.

The numerical predictions indicate that the nonlinear k-ε model predicts a larger recirculation bubble inside the dimple with stronger impingement and upwash flow than the zonal and linear k-ε models. The heat transfer results show that the zonal k-ε model returns weak thermal predictions in all test cases in comparison to other turbulence models. Use of the linear k-ε model leads to improvement in heat transfer predictions inside the dimples and their back rim. However, the most accurate thermal predictions are obtained via the nonlinear k-ε model. As expected, the replacement of the algebraic length-scale correction term with the differential version improves the heat transfer predictions of both linear and nonlinear k-ε models.

The most reliable turbulence model of the current study (i.e. nonlinear k-ε model) may be used for design and optimization of various thermal systems using dimples for heat transfer enhancement (e.g. heat exchangers and internal cooling system of gas turbine blades).

This study aims to use gray models to predict abnormal stock returns.

Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model.

Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models.

The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.

The purpose of this paper is to present a new effective integration method for cyclic plasticity models.

By defining an integrating factor and an augmented stress vector, the system of differential equations of the constitutive model is converted into a nonlinear dynamical system, which could be solved by an exponential map algorithm.

The numerical tests show the robustness and high efficiency of the proposed integration scheme.

The von‐Mises yield criterion in the regime of small deformation is assumed. In addition, the model obeys a general nonlinear kinematic hardening and an exponential isotropic hardening.

Integrating the constitutive equations in order to update the material state is one of the most important steps in a nonlinear finite element analysis. The accuracy of the integration method could directly influence the result of the elastoplastic analyses.

The paper deals with integrating the constitutive equations in a nonlinear finite element analysis. This subject could be interesting for the academy as well as industry. The proposed exponential‐based integration method is more efficient than the classical strategies.