![](/images/graphics-bg.png)
A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network
المؤلفون المشاركون
Rabiei, M. R.
Karbasi, Delara
Nazemi, Alireza
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-27، 27ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-26
دولة النشر
مصر
عدد الصفحات
27
التخصصات الرئيسية
الملخص EN
Bridge regression is a special family of penalized regressions using a penalty function ∑Ajγ with γ≥1 that for γ=1 and γ=2, it concludes lasso and ridge regression, respectively.
In case where the output variable in the regression model was imprecise, we developed a bridge regression model in a fuzzy environment.
We also exhibited penalized fuzzy estimates for this model when the input variables were crisp.
So, we perform the presented optimization problem for the model that leads to a multiobjective program.
Also, we try to determine the shrinkage parameter and the tuning parameter from the same optimization problem.
In order to estimate fuzzy coefficients of the proposed model, we introduce a hybrid scheme based on recurrent neural networks.
The suggested neural network model is constructed based on some concepts of convex optimization and stability theory which guarantees to find the approximate parameters of the proposed model.
We use a simulation study to depict the performance of the proposed bridge technique in the presence of multicollinear data.
Furthermore, real data analysis is used to show the performance of the proposed method.
A comparison between the fuzzy bridge regression model and several other shrinkage models is made with three different well-known fuzzy criteria.
In this study, we visualize the performance of the model by Taylor’s diagram and Bubble plot.
Also, we examine the predictive ability of the model, thus, obtained by cross validation.
The numerical results clearly showed higher accuracy of the proposed fuzzy bridge method compared to the other existing fuzzy regression models: fuzzy bridge regression model, multiobjective optimization, recurrent neural network, stability convergence, and goodness-of-fit measure.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Karbasi, Delara& Rabiei, M. R.& Nazemi, Alireza. 2020. A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network. Journal of Mathematics،Vol. 2020, no. 2020, pp.1-27.
https://search.emarefa.net/detail/BIM-1188231
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Karbasi, Delara…[et al.]. A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network. Journal of Mathematics No. 2020 (2020), pp.1-27.
https://search.emarefa.net/detail/BIM-1188231
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Karbasi, Delara& Rabiei, M. R.& Nazemi, Alireza. A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network. Journal of Mathematics. 2020. Vol. 2020, no. 2020, pp.1-27.
https://search.emarefa.net/detail/BIM-1188231
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1188231
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)