INTELLIGENT NEURO-FUZZY FABRIC EVALUATION SYSTEM: A NOVEL MULTI-DIMENSIONAL STOCHASTIC FUZZY SYSTEM AND A GENERATOR OF TRAINING PATTERNS FOR AN ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.23055/ijietap.2018.25.2.2982Keywords:
backpropagation, fuzzy systems, quality, quantitative characteristics, linguistic label.Abstract
The aim of this paper is to develop a novel multidimensional stochastic Fuzzy Logic System (msFLS) based on normal probability density function to generate multi training patterns of each quality characteristic and used by a neural network. The approach proposed is comprised of three modules. In the first module, a novel multi-dimensional fuzzy system is developed. This approach uses gaussian membership function. Four linguistic labels are used. The fuzzy operation, implication and aggregation method are applied. The fuzzify linguistic outputs obtained are used as target vector T by the second module at two-layer feed-forward backpropagation neural network with a two-element input, ten hidden tansig neurons, and four purelin output neuron used to evaluate and classified each quantitive characteristic. The third module is the validation of the textile quality for multiple goods where the values are defuzzify in a range of 1-10 and classified as linguistic label correspondent. Validation was performed in a knitted textile company in the South of Guanajuato.
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