In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of 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 for forecas...

conditional monitoring is prerequisite to prevent severe damage or falling down the trees. problems such as trunk decays, reduces trees resistance against wind and floods. in this study, a mathematical modeling technique is being selected to determine the trees strength against such events. finally, an applicable model for safety factor was created by having numeral (dimensions of crown and tru...

introduction: the adaptive neuro-fuzzy inference system (anfis) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. in this study we used this model in breast cancer detection. methodology: a set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  first, the risk fact...

This paper introduces a new software product FuzzME. It was developed as a tool for creating fuzzy models of multiple-criteria evaluation and decision making. The type of evaluations employed in the fuzzy models fully corresponds with the paradigm of the fuzzy set theory; the evaluations express the (fuzzy) degrees of fulfillment of corresponding goals. The FuzzME software takes advantage of li...

An inverse minimum spanning tree problem is to make the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, a type of fuzzy inverse minimum spanning tree problem is introduced from a LAN reconstruction problem, where the weights of edges are assumed to be fuzzy variables. The concept of fu...

the railway transportation planning under the fuzzy environment is investigated in this paper. as a main result, a new modeling method, called minimum risk chance-constrained model, is presented based on the credibility measure. for the convenience ofs olving the mathematical model, the crisp equivalents ofc hance functions are analyzed under the condition that the involved fuzzy parameter...

This paper is focused on an introduction of a new software product, which is called FuzzME. This software was developed as a tool for creating fuzzy models of multiple-criteria evaluation and decision making. The type of evaluations employed in the fuzzy models fully corresponds with the paradigm of the fuzzy set theory; the evaluations express the (fuzzy) degrees of fulfillment of correspondin...

Fuzzy logic techniques are efficient in solving complex, ill-defined problems that are characterized by uncertainty of environment and fuzziness of information. Fuzzy logic allows handling uncertain and imprecise knowledge and provides a powerful framework for reasoning. Fuzzy reasoning models are relevant to a wide variety of subject areas such as engineering, economics, psychology, sociology,...