Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Generalized multiscale radial basis function networks
Neural Networks
Expert Systems with Applications: An International Journal
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Prediction of surface roughness in the end milling machining using Artificial Neural Network
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Genetic fuzzy self-tuning PID controllers for antilock braking systems
Engineering Applications of Artificial Intelligence
Predictive Modeling of the Ti6Al4V Alloy Surface Roughness
Journal of Intelligent and Robotic Systems
Expert Systems with Applications: An International Journal
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.