مطالب مرتبط با کلیدواژه

Markov Chain


۱.

Integration of Neural Network, Markov Chain and CA Markov Models to Simulate Land Use Change Region of Behbahan(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهش و برنامه ریزی روستایی سال دهم تابستان ۱۴۰۰ شماره ۳۴ 81-95

کلیدواژه‌ها: Change Detection Neural Network Markov Chain CA Markov Behbehan County

حوزه‌های تخصصی:
تعداد بازدید : ۴۹۹ تعداد دانلود : ۳۸۰
Purpose- Land is the place of earthly natural ecosystem functionality that has been used by humans in multiple methods. Land-use change (LUC) simulation is the most important method for researching LUC, which leads to some environmental issues such as the decreasing supply of forestry products and increasing levels of greenhouse gas emissions. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use-cover (LULC) maps for 2000 and 2014; (ii) assessing Land use changes based on land change modeler (LCM) for the period from 2000 to 2014, and (iii) predicting the plausible land cover pattern in the region of Behbahan, using an algorithm based on ANN for 2028. Design/methodology/approach- A hybrid model consisting of a neural network model, Markov chain (MC), and cellular automata (CA Markov) was designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of an artificial neural network and six variables. The change allocated to each use and the forecasting is computed by Markov chain and CA Markov. Operation model calibration and verification of land use data at two points were conducted in 2000 and 2014. Findings- Modeling results indicate that the model validation phase has a good ability to predict land-use change on the horizon is 14 years old (2028). The comparison between modeling map and map related to 2013 shows that residential area and agricultural land continue to their growth trend so that residential area will be increased from 3157 hectares in 2014 to 4180 hectares in 2028 and it has 2% growth that has been 2% from 2000 to 2014. The results of this study can provide a suitable perspective for planners to manage land use regarding land-use changes in the past, present, and future. They are also can be used for development assessment projects, the cumulative effects assessment, and the vulnerable and sensitive zone recognition.
۲.

A Hybrid Seed Node Selection and No-Retracing Random Walk in Page Rank Algorithm(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Web Research, Volume ۵, Issue ۱,Spring-Summer ۲۰۲۲ 42 - 49

کلیدواژه‌ها: Random Walking Network Sampling Page-Rank Algorithm Clustering Markov Chain

تعداد بازدید : ۳۲۵ تعداد دانلود : ۲۲۴
The random walk technique, which has a reputation for excellent performance, is one method for complex networks sampling. However, reducing the input data size is still a considerable topic to increase the efficiency and speed of this algorithm. The two approaches discussed in this paper, the no-retracing and the seed node selection algorithms, inspired the development of random walk technique. The Google PageRank method is integrated with these different approaches. Input data size is decreased while critical nodes are preserved. A real database was used for this sampling. Significant sample characteristics were also covered, including average clustering coefficient, sampling effectiveness, degree distribution, and average degree. The no-retracing method, for example, performs better. The efficiency increases even further when the no-retracing technique is combined with the Google PageRank. When choosing between public transportation and aircraft, for example, these algorithms might be used since time is crucial. Additionally, these algorithms are more energy-efficient methods that were looked at.
۳.

Statistically Constrained Economic Design of a VSSI X-bar Control Chart Considering Taguchi Loss Function

نویسنده:
منبع: Journal of Data Analytics and Intelligent Decisionmaking, Vol. ۱, Issue. ۳, (۲۰۲۵) 56 - 69

کلیدواژه‌ها: Genetic Algorithm Markov Chain Taguchi loss function Variable sample size and sampling interval (VSSI) X-bar control chart

حوزه‌های تخصصی:
تعداد بازدید : ۸ تعداد دانلود : ۹
In the economic design of control charts, traditional approaches often assume that quality loss remains constant once the quality characteristic surpasses the specification limits. This simplification overlooks the nuanced relationship between deviation magnitude and associated costs. With the increasing adoption of Taguchi’s quality loss function in product design, which quantifies loss as a continuous function of deviation from target values, there is a compelling need to integrate this perspective into control chart methodologies. This paper addresses this gap by developing an economic design framework for control charts that incorporates variable sample sizes and sampling intervals, guided by Taguchi’s quality loss function. The objective is to optimize control chart parameters to minimize the total quality-related costs, including sampling and quality loss costs. To efficiently determine the optimal parameters, a genetic algorithm is employed, with its settings fine-tuned using Taguchi’s orthogonal arrays to enhance convergence and solution quality. The proposed model is rigorously evaluated against traditional fixed sampling interval approaches. Results demonstrate that the variable sampling strategy, informed by Taguchi’s loss function, significantly improves cost efficiency and quality control effectiveness. This integration offers a more realistic and economically sound approach to control chart design, accommodating the continuous nature of quality loss and enabling dynamic sampling adjustments. The findings underscore the potential of combining advanced optimization techniques with robust quality loss modeling to advance statistical process control practices in manufacturing and service industries.