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On A New Framework of Autoregressive Fuzzy Time Series Models
Qiang Song 대한산업공학회 2014 Industrial Engineeering & Management Systems Vol.13 No.4
Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternatives and this is the objective of this paper. By transforming a fuzzy number to a real number via integrating the inverse of the membership function, new autoregressive models can be developed to fit the observation values of a fuzzy time series. With the new models, the issues of model identification and parameter estimation can be addressed; and trends, seasonalities and multivariate fuzzy time series could also be modeled with ease. In addition, asymptotic behaviors of fuzzy time series can be inspected by means of characteristic equations.
On A New Framework of Autoregressive Fuzzy Time Series Models
Song, Qiang Korean Institute of Industrial Engineers 2014 Industrial Engineeering & Management Systems Vol.13 No.4
Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternatives and this is the objective of this paper. By transforming a fuzzy number to a real number via integrating the inverse of the membership function, new autoregressive models can be developed to fit the observation values of a fuzzy time series. With the new models, the issues of model identification and parameter estimation can be addressed; and trends, seasonalities and multivariate fuzzy time series could also be modeled with ease. In addition, asymptotic behaviors of fuzzy time series can be inspected by means of characteristic equations.
A New Algorithm for Automated Modeling of Seasonal Time Series Using Box-Jenkins Techniques
Song, Qiang,Esogbue, Augustine O. Korean Institute of Industrial Engineers 2008 Industrial Engineeering & Management Systems Vol.7 No.1
As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.
Song, Zhi-Qiang,Cheng, Ju-E,Cheng, Fei-Xue,Zhang, De-Yong,Liu, Yong The Korean Society of Plant Pathology 2017 Plant Pathology Journal Vol.33 No.2
Tylenchulus semipenetrans is an important and widespread plant-parasitic nematode of citrus worldwide and can cause citrus slow decline disease leading to significant reduction in tree growth and yield. Rapid and accurate detection of T. semipenetrans in soil is important for the disease forecasting and management. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed to detect T. semipenetrans using DNA extracted from soil. A set of five primers was designed from the internal transcribed spacer region (ITS1) of rDNA, and was highly specific to T. semipenetrans. The LAMP reaction was performed at $63^{\circ}C$ for 60 min. The LAMP product was visualized directly in one reaction tube by adding SYBR Green I. The detection limit of the LAMP assay was $10^{-2}J2/0.5g$ of soil, which was 10 times more sensitive than conventional PCR ($10^{-1}J2/0.5g$ of soil). Examination of 24 field soil samples revealed that the LAMP assay was applicable to a range of soils infested naturally with T. semipenetrans, and the total assay time was less than 2.5 h. These results indicated that the developed LAMP assay is a simple, rapid, sensitive, specific and accurate technique for detection of T. semipenetrans in field soil, and contributes to the effective management of citrus slow decline disease.
Lessons Learned and Challenges Encountered in Retail Sales Forecast
Song, Qiang Korean Institute of Industrial Engineers 2015 Industrial Engineeering & Management Systems Vol.14 No.2
Retail sales forecast is a special area of forecasting. Its unique characteristics call for unique data models and treatment, and unique forecasting processes. In this paper, we will address lessons learned and challenges encountered in retail sales forecast from a practical and technical perspective. In particular, starting with the data models of retail sales data, we proceed to address issues existing in estimating and processing each component in the data model. We will discuss how to estimate the multi-seasonal cycles in retail sales data, and the limitations of the existing methodologies. In addition, we will talk about the distinction between business events and forecast events, the methodologies used in event detection and event effect estimation, and the difficulties in compound event detection and effect estimation. For each of the issues and challenges, we will present our solution strategy. Some of the solution strategies can be generalized and could be helpful in solving similar forecast problems in different areas.
Bacterial Foraging Optimization Based on LS-SVM for BTP Forecasting
SONG Qiang,GUO Xiao-bo,LI hua 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.1
Because of the nonlinear characteristics of the BTP in sintering process, the BTP forecasting is difficult to realize. The LS-SVM was employed in this study for forecasting. However, Because SVM is using the two programming support vector, computing and solving two quadratic programming will involve matrix of order m, when the M number is large storage and computing the matrix will consume a large amount of computer memory and calculation time. The traditional training methods based on searching technique are not effective and fast. Therefore, bacterial foraging optimization (BFO) was adopted to optimize the LS-SVM. BFO is a novel and powerful global search technique, It is found that Bacteria Foraging Algorithm (BFO) is capable of improving the speed of convergence as well as the precision in the desired result. Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems. It can conclude that BFO is effective and rapid for the cluster analysis problem.
WSN Coverage Optimization Strategy Based on Improved Artificial Fish Swarm Algorithm
Qiang Song,Lingxia Liu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.8
This paper presents a kind of coverage optimization strategy based on improved artificial fish swarm algorithm for wireless sensor networks, by adaptively adjusting the vision range and the step length of artificial fish swarm the accuracy of optimization, convergence speed and stability are improved, then combining with the performance of WSN network coverage, the network coverage can be optimized. The simulation results show that comparing to the basic artificial fish swarm algorithm, the network coverage ratio of improved artificial fish swarm algorithm improves 17%.