Equivalent method for accurate solution to linear interval equations

Based on linear interval equations, an accurate interval finite element method for solving structural static problems with uncertain parameters in terms of optimization is discussed. On the premise of ensuring the consistency of solution sets, the original interval equations are equivalently transformed into some deterministic inequations. On this basis, calculating the structural displacement response with interval parameters is predigested to a number of deterministic linear optimization problems. The results are proved to be accurate to the interval governing equations. Finally, a numerical example is given to demonstrate the feasibility and efficiency of the proposed method.

Weak optimal inverse problems of interval linear programming based on KKT conditions

In this paper,weak optimal inverse problems of interval linear programming(IvLP)are studied based on KKT conditions.Firstly,the problem is precisely defined.Specifically,by adjusting the minimum change of the current cost coefficient,a given weak solution can become optimal.Then,an equivalent characterization of weak optimal inverse IvLP problems is obtained.Finally,the problem is simplified without adjusting the cost coefficient of null variable.

Global stability of interval recurrent neural networks

The robust global exponential stability of a class of interval recurrent neural networks（RNNs） is studied,and a new robust stability criterion is obtained in the form of linear matrix inequality.The problem of robust stability of interval RNNs is transformed into a problem of solving a class of linear matrix inequalities.Thus,the robust stability of interval RNNs can be analyzed by directly using the linear matrix inequalities（LMI） toolbox of MATLAB.Numerical example is given to show the effectiveness of the obtained results.

An Optimal Model for Carbon Sinks of CDM Project to Build a New Countryside Based on Cyclic Economy under Uncertainty

Based on the status and characteristics of Clean Development Mechanism （CDM） renewable energy project and the mode of cyclic economy, aimed at achieving the maximum capacity of carbon sinks in the system, Lvjin Jiayuan—New Countryside Distripark CDM project in Wuchuan County was taken as an example for developing an interval linear programming （ILP） model to optimize the crops planting scheme and cows breeding scheme by using interval optimal method. The case showed that the optimized crops planting scheme and cows breeding scheme obtained from the optimal model was reasonable with relatively preferable overall performance. In the context of meeting economic benefits and fertilizer, electricity demand, [231 287.8, 273 312.7] t of CO2 could be absorbed and fixed, which had increased by [12.94, 33.46]% compared with the feasibility scheme of case project and provided technical support for making the decision in CDM project.

A Hybrid Inexact Optimization Model for Land-use Allocation of China

Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China considering various political, environmental, ecological and economic conditions have become research topics with great significance. In this study, an interval fuzzy national-scale land-use model(IFNLM) was developed for optimizing land systems of China. IFNLM is based on an integration of existing interval linear programming(ILP), and fuzzy flexible programming(FFP) techniques. IFNLM allows uncertainties expressed as discrete interval values and fuzzy sets to be incorporated within a general optimization framework. It can also facilitate national-scale land-use planning under various environmental, ecological, social conditions within a multi-period and multi-option context. Then, IFNLM was applied to a real case study of land-use planning in China. The satisfaction degree of environmental constraints is between 0.69 and 0.97, the system benefit will between 198.25 × 1012 USD and 229.67 × 1012 USD. The results indicated that the hybrid model can help generate desired policies for land-use allocation with a maximized economic benefit and minimized environmental violation risk. Optimized land-use allocation patterns can be generated from the proposed IFNLM.

A refined risk explicit interval linear programming approach for optimal watershed load reduction with objective-constraint uncertainty tradeoff analysis

To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming （REILP） approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeofffactor （α） into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty- based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of a was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demon- strate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.

Predictive simulation and optimal allocation of surface water resources in reservoir basins under climate change

Predicting and allocating surface water resources are becoming increasingly important tasks for addressing the risk of water shortages and challenges of climate change,especially in reservoir basins.However,surface water resource management includes many systematic uncertainties and complexities that are difficult to address.Thus,advanced models must be developed to support predictive simulations and optimal allocations of surface water resources,which are urgently required to ensure regional water supply security and sustainable socioeconomic development.In this study,a soil and water assessment tool-based interval linear multi-objective programming(SWAT-ILMP)model was developed and integrated with climate change scenarios,SWAT,interval parameter programming,and multi-objective programming.The developed model was applied to the Xinfengjiang Reservoir basin in South China and was able to identify optimal allocation schemes for water resources under different climate change scenarios.In the forecast year 2025,the optimal water quantity for power generation would be the highest and account for∼60%of all water resources,the optimal water quantity for water supply would account for∼35%,while the optimal surplus water released from the reservoir would be the lowest at≤5%.In addition,climate change and reservoir initial storage would greatly affect the surplus water quantity but not the power generation or water supply quantity.In general,the SWAT-ILMP model is applicable and effective for water resource prediction and management systems.The results from different scenarios can provide multiple alternatives to support rational water resource allocation in the study area.

INTERVAL TIME SERIES ANALYSIS WITH AN APPLICATION TO THE STERLING-DOLLAR EXCHANGE RATE

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.