A methodology for the development of linguistically interpretable fuzzy models from data is presented. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the /rst phase, the structure of the model is obtained by means of subtractive clustering, which allows the extraction of a set...

Fuzzy Petri Net for Web Service Composition (FPN4WSC) aims to compose the individual web services into more complex one. It is a workflow model which is hybridization between Petri net, SHOP2, and fuzzy logic. Petri net allows user to specify his request as a workflow. SHOP2 is used as an Artificial Intelligence (AI) planning system to get a plan for the user request. However, SHOP2 fails to ca...

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some...

A fuzzy goal programming approach is applied in this paper for solving the vendor selection problem with multiple objectives, in which some of the parameters are fuzzy in nature. A vendor selection problem has been formulated as a fuzzy mixed integer goal programming vendor selection problem that includes three primary goals: minimizing the net cost, minimizing the net rejections, and minimizin...

A framework of integrated expert systems based on fuzzy Petri net, called fuzzy Petri net based expert system (FPNES) are proposed for bridge damage assessment. Major features of FPNES include: reasoning for uncertain and imprecise information; knowledge representation through the use of hierarchical fuzzy Petri nets; reasoning mechanism based on fuzzy Petri nets; and explanation of reasoning p...

The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization problems. In the proposed GA, values in the genotypes are not real numbers but fuzzy numbers. Evolutionary processes in GA are extended so that GA can handle genotype instances with fuzzy numbers. The proposed method is applied to evolving neural networks with fuzzy weights and biases. Experimental ...

in this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the takagi-sugeno (t-s) fuzzy models is considered. a novel linear matrix inequality (lmi)-based stability criterion is obtained by using lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...