Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

A Model for Retrieval of Dual Linear Polarization Radar Fields from Model Simulation Outputs

An algorithm for retrieving polarimetric variables from numerical model fields is developed. By using this technique, radar reflectivity at horizontal polarization~ differential reflectivity, specific differential phase shift and correlation coefficients between the horizontal and vertical polarization signals at zero lag can be derived from rain, snow and hail contents of numerical model outputs. Effects of environmental temperature and the melting process on polarimetric variables are considered in the algorithm. The algorithm is applied to the Advanced Regional Prediction System （ARPS） model simulation results for a hail storm. The spatial distributions of the derived parameters are reasonable when compared with observational knowledge. This work provides a forward model for assimilation of dual linear polarization radar data into a mesoscale model.

Prediction of efficient outputs based on GM(1,N) model and weak DEA efficiency

This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient outputs.The increased outputs are solved by linear programming using data envelopment analysis efficiency theories,wherein a new sample is introduced whose inputs are equal to the budget in the issue No.n ＋ 1 and outputs are forecasted by the GM（1,N） model.The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome.The new prediction method provides decision-makers with more decisionmaking information,and the initial conditions are easy to be given.

Prediction of Total Output Value of Construction Industry in Jiangxi Province Based on Grey Prediction Model

In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.

Application of multi-outputs LSSVR by PSO to the aero-engine model

Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.

Prediction Model of Data Envelopment Analysis with Undesirable Outputs

Data envelopment analysis (DEA) has become a standard non parametric approach to productivity analysis, especially to relative efficiency analysis of decision making units (DMUs). Extended to the prediction field, it can solve the prediction problem with multiple inputs and outputs which can not be solved easily by the regression analysis method.But the traditional DEA models can not solve the problem with undesirable outputs,so in this paper the inherent relationship between goal programming and the DEA method based on the relationship between multiple goal programming and goal programming is explored,and a mixed DEA model which can make all factors of inputs and undesirable outputs decrease in different proportions is built.And at the same time,all the factors of desirable outputs increase in different proportions.

Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs

The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

Application of Gray Metabolic Model in the Prediction of the Cotton Output in China

In order to forecast the cotton output of China in the year 2011, Gray Metabolic Forecast Model is established based on both the Gray Forecast Model and the Metabolic Theory. According to the actual situation, forecast results of conventional GM (1, 1) Model and Metabolism GM (1, 1) Model are analyzed, showing that Metabolic Forecast Model has higher precision than the conventional forecast model. Therefore, Metabolism GM (1, 1) Model is used to forecast the cotton output of China in the year 2011, which is 614 968.3 thousand tons.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

Credit Risk Model Taking Account of Inflation and Its Contribution to Macroeconomic Discussion on Effect of Inflation on Output Growth

We use Extended Merton model(EMM)for estimating the firm’s credit risks in the presence of inflation.We show quantitatively that inflation is an influential factor making either a benign or adverse effect on the firm’s survival,supporting at the microeconomic level New Keynesian findings of the nonlinear inflation effect on output growth.Lower inflation increasing the firm’s expected rate of return can raise its mean year returns and decrease its default probability.Higher inflation,decreasing the expected rate return,makes the opposite effect.The magnitude of the adverse effect depends on the firm strength:for a steady firm,this effect is small,whereas for a weaker firm,it can be fatal.EMM is the only model taking account of inflation.It can be useful for banks or insurance companies estimating credit risks of commercial borrowers over the debt maturity,and for the firm’s management planning long-term business operations.

Integrated Water and CGE Model of the Impacts of Water Policy on the Beijing's Economy and Output

The article used general equilibrium model to analyze the change of gross domestic product and industry output affected by water resources policies in Beijing City by using GEMPACK soft tool.The article researches on rules of water supply and demand,evaluating water resources,building water resources input and output table,establishing water computable general equilibrium model and stimulating water policy.The stimulation gives a scenario that increases water price by 10%.The result shows the following aspects.First,water resources policy infects gross domestic product and industry output in different ways.There are different behaviors in different industries as to the water policy. Agriculture industry has the same tendency as water price change and it has more sensitive to water quantity than to water price.For basic energy industries such as oil and chemistry and gas,they show diversity tendency.As to some high water consumer industry such as paper and textile etc.,water resource economic policy can infect them greatly and can promote them to accomplish more water-saving technology.Waste water and construction and service industries show the same tendency as to water policy.Second,government should pay more attention to water resource policy by macro economic administration.The simulation also shows that the output and supply and consumer price change more than expect as to water policy in a free market economic in water industry.So as to a government policy maker,one should be more carefully and prepare suitable forecast and plan to water policy and its negative impact.

Iterative identification of discrete-time output-error model with time delay

The problem of discrete-time model identification of industrial processes with time delay was investigated.An iterative and separable method is proposed to solve this problem,that is,the rational transfer function model parameters and time delay are alternately fixed to estimate each other.The instrumental variable technique is applied to guarantee consistent estimation against measurement noise.A noteworthy merit of the proposed method is that it can handle fractional time delay estimation,compared to existing methods commonly assuming that the time delay is an integer multiple of the sampling interval.The identifiability analysis for time delay is addressed and correspondingly,some guidelines are provided for practical implementation of the proposed method.Numerical and experimental examples are presented to illustrate the effectiveness of the proposed method.

Output regulation of nonlinear singularly perturbed systems based on T-S fuzzy model

Based on the T-S model, the output regulation of nonlinear singularly perturbed systems via state feedback is discussed. It is shown that, under standard assumptions, this problean is solvable if and only if certain linear matrix equations are solvable. Once these equations are solvable, the state feedback regulator can easily be constructed.

Environmental input-output model and its analysis with a focus on the solid waste management sectors

An environmental input output model (EIOM) was introduced to the regional solid waste management sectors, which can reflect the direct and indirect relations between the environment and the regional economy development. Some details about how to use the EIOM was discussed. The EIOM was applied to the Changsha City in China. The example results indicate that much useful information related to the environment and the regional economy development can be gained from the solution of the EIOM. Thus, the EIOM can be used as a useful tool for the sustainable development planning including the solid waste management sectors.

Construction of Two-Region Input-output Model——A Case Study of Henan Province

On the basis of input-output table of Henan Province and China in 2007, this paper advances a simple method of constructing two-region input-output model using MRIO model, to research the economic link between the industries of Henan Province and the industries of other regions. I summarize the characteristics of this method based on this as follows: when researching inter-regional economic link, the multi-region or two-region input-output model has prominent superiority, and we can conduct preliminary estimation on the multi-region input-output model using location quotient approach.

The Design of Output Feedback Distributed Model Predictive Controller for a Class of Nonlinear Systems

For a class of nonlinear systems whose states are immeasurable, when the outputs of the system are sampled asynchronously, by introducing a state observer, an output feedback distributed model predictive control algorithm is proposed. It is proved that the errors of estimated states and the actual system's states are bounded. And it is guaranteed that the estimated states of the closed-loop system are ultimately bounded in a region containing the origin. As a result, the states of the actual system are ultimately bounded. A simulation example verifies the effectiveness of the proposed distributed control method.

The Influences of Energy Price Variation on the Prices of Other Industries: A Study Based on Input-Output Price Model

Energy has laid material foundation for human society during its development. Meanwhile, any change of price in the energy industry may influence social production and people’s life at all levels via an input-output mechanism under which the change related to energy is surely transmitted to other industries. The price change thus incurred in all industries may adversely affect the realization of macroeconomic objective-maintaining prices at a stable level. It is, therefore, needed to conduct an empirical research related to the impact of price change in energy industry on that in other industries. According to the data coming from “China’s 2015 Input-Output Extension Table (42 Departments)” and four hypothetical basis, this article focuses on four energy sectors and analyzes how deeply the price change of them, by use of input-output model, affects that of other industrial products under five conditions where each of their price rises by 10% individually or simultaneously, and why such an influence occurs. The results show that the price rising of the energies in question leads to an upward growth in the prices of other industrial products, especially when their prices go up simultaneously. Besides, the price increase in the four energy sectors doesn’t influence other industries in an accumulation form but actually leads to a rollback in some of other industries. It is recommended to adopt diversified pricing strategies for different energy products, thus maximizing the value of each specific energy, and meanwhile achieving the goals of energy consumption reduction and price equilibrium.

Static output feedback stabilization for second-order singular systems using model reduction methods

In this paper,the static output feedback stabilization for large-scale unstable second-order singular systems is investigated.First,the upper bound of all unstable eigenvalues of second-order singular systems is derived.Then,by using the argument principle,a computable stability criterion is proposed to check the stability of secondorder singular systems.Furthermore,by applying model reduction methods to original systems,a static output feedback design algorithm for stabilizing second-order singular systems is presented.A simulation example is provided to illustrate the effectiveness of the design algorithm.

Establishment of Output Models and Constraints Analysis for Independent Wind/PV/Storage System

In remote areas far from the grid, wind/PV/storage generating system is relatively a good choice, whatever in resource configuration, performance or prices. For the independent hybrid power system, the output models of wind turbines, photovoltaic arrays and batteries are built in this paper, and based on the objectives of the capacity configuration optimal model, constraints used in the process of capacity configuration are analyzed. These provide convenient conditions and theoretical basis for the optimal capacity configuration of independent wind/PV/storage system.