Traditional active flutter suppression controllers are designed based on model. However, as the aircraft becomes more and more powerful, the modeling of aeroelastic system becomes difficult and the model-free requirement of controller design becomes more and more urgent. The complexity of industrial processes has brought about massive operational data generated online. Aviation industry development has entered the era of big data. Breaking through the traditional theoretical framework, mining the correlation, evolution and dynamic characteristics of the system from the data is the inevitable choice to meet this demand. In this paper, a data-driven model-free controller is designed, which relies on ridge regression of the input and output variation at each operating point of the closed-loop controlled system to recursively derive the iterative format of the control signals and ensure the numerical stability of the signals. The controller can only use the real-time measurement of the system’s online input and output data for continuous correction, to achieve the purpose of flutter suppression. Then flutter suppression of a three-degree-of-freedom binary wing with a control surface is studied, and the superiority of model-free controller is demonstrated by comparing it with the optimal controller.

In this paper we analyse insurance claim frequency data using the bivariate negative binomial regression (BNBR) model. We use general insurance data on claims from simple third-party liability insurance and comprehensive insurance. We find that bivariate regression, with its capacity for modelling correlation between the two observed claim counts, provides both a superior fit and out-of-sample prediction compared with the more common practice of fitting univariate negative binomial regression models separately to each claim type. Noting the complexity of BNBR models and their potential for a large number of parameters, we explore the use of model shrinkage methodology, namely the least absolute shrinkage and selection operator (Lasso) and ridge regression. We find that models estimated using shrinkage methods outperform the ordinary likelihood-based models when being used to make predictions out-of-sample. We find that the Lasso performs better than ridge regression as a method of shrinkage.

This study examines the valuation of research as intangible capital in agriculture using Tobin's q theory. The market value of public research capital is estimated to be 8.6 times higher than conventional assets. Private research capital is valued 5.2 times higher than conventional assets. The estimated valuation multiplier for all farm assets dropped 1/3 over the last decade. In recent years the valuation multiplier has dropped below a dollar, which indicates the market is undervaluing farm assets.

The majority of decisions concerning investment and allocation of public funds for agricultural research, extension, and teaching (RET) are made at the state-level, while most of the quantitative RET evaluations are made on a national basis. This paper illustrates an approach for conducting a disaggregated state-level evaluation of agricultural research, extension, and teaching. Ridge regression is employed to handle multicollinearity problems.