Information Sciences: an International Journal
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Information Sciences: an International Journal
A linear regression model using triangular fuzzy number coefficients
Fuzzy Sets and Systems
Fuzzy regression wiht radial basis function network
Fuzzy Sets and Systems
Extended fuzzy regression models using regularization method
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
An enhanced fuzzy linear regression model with more flexible spreads
Fuzzy Sets and Systems
Reduction to least-squares estimates in multiple fuzzy regression analysis
IEEE Transactions on Fuzzy Systems
Fuzzy regression models using the least-squares method based on the concept of distance
IEEE Transactions on Fuzzy Systems
Real-time fuzzy switching regression analysis: a convex hull approach
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Effects of attribute reducing on real-estate valuation
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
Expert Systems with Applications: An International Journal
Fuzzy linear regression based on Polynomial Neural Networks
Expert Systems with Applications: An International Journal
A linear regression model for nonlinear fuzzy data
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Tackling outliers in granular box regression
Information Sciences: an International Journal
Fuzzy least-absolutes regression using shape preserving operations
Information Sciences: an International Journal
Kernel based nonlinear fuzzy regression model
Engineering Applications of Artificial Intelligence
International Journal of Approximate Reasoning
A two-stage approach for formulating fuzzy regression models
Knowledge-Based Systems
A non-linear fuzzy regression for estimating reliability in a degradation process
Applied Soft Computing
Hi-index | 0.21 |
In this paper, we propose an iterative algorithm for multiple regression with fuzzy variables. While using the standard least-squares criterion as a performance index, we pose the regression problem as a gradient-descent optimisation. The separation of the evaluation of the gradient and the update of the regression variables makes it possible to avoid undue complication of analytical formulae for multiple regression with fuzzy data. The origins of fuzzy input data are traced back to the fundamental concept of information granulation and an example FCM-based granulation method is proposed and illustrated by some numerical examples. The proposed multiple regression algorithm is applied to one-, three- and nine-dimensional synthetic data sets as well as the 13-dimensional Boston Housing dataset from the machine learning repository. The algorithm's performance is illustrated by the corresponding plots of convergence of regression parameters and the values of the prediction error of the resulting regression model. General comments on the numerical complexity of the proposed algorithm are also provided.