重庆市浅埋地下洞室安全顶板厚度研究

本文在参考已有资料的基础上, 分析了重庆市浅埋地下洞室安全顶板厚度的影响因素。采用有限单元数值模拟法研究了地基与洞室相互作用下的破坏机制, 以及各影响因素对浅埋地下洞室安全顶板厚度的影响及相关变化规律, 并用逐步线性回归的方法得出了一个能综合考虑各影响因素的安全顶板厚度的预测模型。本文最后还分析了顶拱裂隙对安全顶板厚度的影响。     

Trend mining for system reliability employing a novel heuristic-based Kriging profiling method

This paper presents a novel integration of heuristic-based regressor for the prediction of system reliability. This is implemented by integrating single layer perceptron (SLP) into Kriging model on the basis of an enhanced Particle Swarm Optimization. The proposed method is labeled here as heuristic SLP-based Kriging, or in short HSK. The backbone of HSK is a Competitive Niching-inspired PSO (CNPSO) that serves as the heuristic for identifying the core parameters of the SLP-based Kriging. CNPSO is composed of an opposition-based competitive initialization and a niching-inspired search scheme. For practicality and validation purposes, realistic datasets in the literature of system reliability are considered in the present study. The experimental results obtained demonstrated that HSK outperformed state-of-the-art methods proposed in the literature for addressing the same issue.     

Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate,interval-valued time series

Time series are built as a result of real-valued observations ordered in time; however, in some cases, the values of the observed variables change significantly, and those changes do not produce useful information. Therefore, within defined periods of time, only those bounds in which the variables change are considered. The temporal sequence of vectors with the interval-valued elements is called a ‘multivariate interval-valued time series.’ In this paper, the problem of forecasting such data is addressed. It is proposed to use fuzzy grey cognitive maps (FGCMs) as a nonlinear predictive model. Using interval arithmetic, an evolutionary algorithm for learning FGCMs is developed, and it is shown how the new algorithm can be applied to learn FGCMs on the basis of historical time series data. Experiments with real meteorological data provided evidence that, for properly-adjusted learning and prediction horizons, the proposed approach can be used effectively to the forecasting of multivariate, interval-valued time series. The domain-specific interpretability of the FGCM-based model that was obtained also is confirmed.     

Combining a regression model with a multivariate Markov chain in a forecasting problem

This paper proposes a new concept: the usage of Multivariate Markov Chains (MMC) as covariates. Our approach is based on the observation that we can treat possible categorical (or discrete) regressors, whose values are unknown in the forecast period, as an MMC in order to improve the forecast error of a certain dependent variable. Hence, we take advantage of the information about the past state interactions between the MMC categories to forecast the categorical (or discrete) regressors and improve the forecast of the actual dependent variable.     

中国养老保险隐性债务未来规模的预测

伴随转制而产生的隐性债务问题,正在成为制约中国养老保险制度健康发展的"瓶颈".解决养老保险隐性债务的基本前提是要测算债务规模.本文建立养老保险隐性债务的预测模型,对中国养老保险隐性债务规模进行预测并得到结果.     

耐用消费品拥有量预测的研究

本文在分析耐用消费品拥有量增长特性基础上，提出三种假设模型，并给出三个例子，说明应用这些假设模型对我国主要耐用消费品的拥有量进行预测的方法。     

A fuzzy clustering approach to real-time demand-responsive bus dispatching control

Quick response (QR) to passenger needs is a key objective for advanced public transportation systems (APTS), and it has become increasingly important for contemporary metropolitan bus operations to gain a competitive advantage over private transportation. This paper presents a real-time control methodology for demand-responsive bus operations that respond quickly to passenger needs. The proposed method primarily involves two levels of functionality: (1) short-term forecasting of passenger demands using time-series prediction models, and (2) identification of service strategies coupled with the associated bus service segments using fuzzy clustering technologies in response to variances in passenger demand attributes and traffic conditions. The proposed bus operations method identifies the demand-responsive vehicle service strategies primarily according to the predicted up-to-date attributes of passengers’ demands, rather than deterministic passenger arrival rates, which were generally used in previous literature. In addition, the variation of traffic conditions along bus lines is considered in the proposed method. Results from numerical studies using real data of passengers’ demands, including passenger volume at each bus stop and the passenger origin-destination (O-D) patterns, are presented to demonstrate the effectiveness of the proposed method for real-world applications.     

Estimation of the most influential factors on the laser cutting process heat affected zone (HAZ) by adaptive neuro-fuzzy technique

Heat affected zone (HAZ) of the laser cutting process may be developed on the basis on combination of different factors. In this investigation was analyzed the HAZ forecasting based on the different laser cutting parameters. The main aim in this article was to analyze the influence of three inputs on the HAZ of the laser cutting process. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for HAZ forecasting. Three inputs are considered: laser power, cutting speed and gas pressure. According the results the cutting speed has the highest influence on the HAZ forecasting (RMSE: 0.0553). Gas pressure has the smallest influence on the HAZ forecasting (RMSE: 0.0801). The results can be used in order to simplify HAZ prediction and analyzing.     

A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM) is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1) In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index) of National Stock Exchange of India (NSE) is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.     

Mortality density forecasts: An analysis of six stochastic mortality models

This paper develops a framework for developing forecasts of future mortality rates. We discuss the suitability of six stochastic mortality models for forecasting future mortality and estimating the density of mortality rates at different ages. In particular, the models are assessed individually with reference to the following qualitative criteria that focus on the plausibility of their forecasts: biological reasonableness; the plausibility of predicted levels of uncertainty in forecasts at different ages; and the robustness of the forecasts relative to the sample period used to fit the model. An important, though unsurprising, conclusion is that a good fit to historical data does not guarantee sensible forecasts. We also discuss the issue of model risk, common to many modelling situations in demography and elsewhere. We find that even for those models satisfying our qualitative criteria, there are significant differences among central forecasts of mortality rates at different ages and among the distributions surrounding those central forecasts.