نتایج جستجو برای: hybrid models
تعداد نتایج: 1081986 فیلتر نتایج به سال:
the efficiency measurement is a subject of great interest. the majority of studies on dea models have been carried out using radial or non-radial approaches regarding the application of dea for the efficiency measurement. this paper, based on the directional distance function, proposes a new generalized hybrid measure of efficiency under generalized returns to scale with the existence of both r...
هدف پژوهش حاضر پیشبینی شاخص قیمت بورس اوراق بهادار تهران با استفاده از مدل شبکه عصبی هیبریدی مبتنی بر الگوریتم ژنتیک و جستجوی هارمونی است. مربوطترین نماگرهای تکنیکی به عنوان متغیرهای ورودی و تعداد بهینه نرون در لایه پنهان شبکه عصبی مصنوعی با استفاده از الگوریتمهای فراابتکاری ژنتیک و جستجوی هارمونی حاصل میگردد. مقادیر روزانه شاخص قیمت بورس اوراق بهادار تهران از تاریخ 1/10/91 الی 30/9/94 جهت ...
rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. in this study statistical method armax model, neural network, neuro-fuzzy (anfis subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. in each method optimum structure was deter...
abstract this paper discusses several commonly used models for strategic marketing¹ including market environmental analysis methods (i.e. swot and pest analysis) and strategic marketing tools and techniques (i.e. boston matrix and shell directional policy matrix)and shows how these models may help a firm to achieve its strategic goals. at first, the main reason for doing this research is de...
The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intell...
The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...
a three-phase hybrid times series modeling framework for improved hospital inventory demand forecast
background and objectives: efficient cost management in hospitals’ pharmaceutical inventories have thepotential to remarkably contribute to optimization of overall hospital expenditures. to this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. while the linear methods are frequently used for forecasting purposes chiefly due to their sim...
Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstl...
The efficiency measurement is a subject of great interest. The majority of studies on DEA models have been carried out using radial or non-radial approaches regarding the application of DEA for the efficiency measurement. This paper, based on the directional distance function, proposes a new generalized hybrid measure of efficiency under generalized returns to scale with the existence of both r...
One of the main concerns of investors is the evaluation of the return on investment, which is conducted using various models such as the CAPM (single-factor model), Fama-French three/five-factor models, and Roy and Shijin’s six-factor model and other models known as multi-factor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, ...
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