An intelligent approach to integration and control of textile processes
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
Control of chaotic dynamical systems using radial basis function network approximators
Information Sciences: an International Journal
Time-series forecasting using GA-tuned radial basis functions
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Dynamic system identification via recurrent multilayer perceptrons
Information Sciences—Informatics and Computer Science: An International Journal
Stochastic simulations of web search engines: RBF versus second-order regression models
Information Sciences—Informatics and Computer Science: An International Journal
Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Numerical Methods for Engineers
Numerical Methods for Engineers
A machine vision quality control system for industrial acrylic fibre production
EURASIP Journal on Applied Signal Processing
Regularization in the selection of radial basis function centers
Neural Computation
Information Sciences: an International Journal
Hi-index | 0.07 |
In this paper, experimental, computational intelligence based and statistical investigations of warp tensions in different back-rest oscillations are presented. Firstly, in the experimental stage, springs having different stiffnesses are used to obtain different back-rest oscillations. For each spring, fabrics are woven in different weft densities and the warp tensions are measured and saved during weaving process. Secondly, in the statistical investigation, the experimental data are analyzed by using linear multiple and quadratic multiple-regression models. Later, in the computational intelligence based investigation, the data obtained from the experimental study are analyzed by using artificial neural networks that are universal approximators which provide a massively parallel processing and decentralized computing. Specially, radial basis function neural network structure is chosen. In this structure, cross-validation technique is used in order to determine the number of radial basis functions. Finally, the results of regression analysis, the computational intelligence based analysis and experimental measurements are compared by using the coefficient of determination. From the results, it is shown that the computational intelligence based analysis indicates a better agreement with the experimental measurement than the statistical analysis.