In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance to<b...

in this paper, a new approach of modeling for artificial neural networks (ann) models based on the concepts of ann and fuzzy regression is proposed. for this purpose, we reformulated ann model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ann models. hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility. in ...

considering convergent product as an important manufacturing technology for digital products, we integrate functions and sub-functions using a comprehensive fuzzy mathematical optimization process. to form the convergent product, a web-based fuzzy network is considered in which a collection of base functions and sub-functions configure the nodes and each arc in the network is to be a link betwe...

this paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. the output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (llnf) model, is useful for assessing a project status at different time horizons. being trained by a locally linear model tree (lolimot) learning algorithm, the model is int...

Amongst possible choices for identifying complicated processes for prediction, simulation, and approximation applications, high-order Takagi-Sugeno (TS) fuzzy models are fitting tools. Although they can construct models with rather high complexity, they are not as interpretable as first-order TS fuzzy models. In this paper, we first propose to use Deformed Linear Models (DLMs) in consequence pa...

nowadays, the problem of the flexibility in any organization has essential importance and the associated investigation with the organizational efficiency and performance is considered. in real-world situation, the process of decision making is often based on linguistic and mental data, so applying fuzzy logic in modeling at this point of view is convenient. in this paper, a fuzzy approach is ap...

Recently, the interest in data-driven approaches to the modeling of nonlinear processes has increased. Techniques based on fuzzy sets and rule-based systems have proven suitable mainly because of their potential to yield transparent models that are at the same time reasonably accurate. Many of the data-driven fuzzy modeling algorithms, however, aim primarily at good numerical approximation, whi...

For the successful tree establishment, an evaluation of land suitability is necessary.In this paper, we demonstrate how to implement fuzzy classification of land suitability in aGIS environment for afforestation with Juglans regia in Gharnaveh Watershed of GolestanProvince in Iran. Juglans regia is one of the most important agro-forestry species in manyrural parts of Iran. Relevant criteria for...

We introduce a new method for modeling rating (utility) functions which employs techniques from fuzzy set theory. The main idea is to build a hierarchical model, called a fuzzy operator tree (FOT), by recursively decomposing a rating criterion into sub-criteria, and to combine the evaluations of these sub-criteria by means of suitable aggregation operators. Apart from the model conception itsel...