Robust fuzzy modeling and symbolic regression for establishing accurate and interpretable prediction models in supervising tribological systems

Keyphrases

• Symbolic Regression 100%
• Tribological Systems 100%
• Robust Fuzzy Modeling 100%
• Interpretable Prediction Model 100%
• Prediction Accuracy 100%
• Friction Coefficient 50%
• Genetic Programming 25%
• Model Complexity 25%
• Regression Model 25%
• Modeling Techniques 25%
• Friction Wear 25%
• Learning Scheme 25%
• Regularized Learning 25%
• Nonlinear Regression 25%
• Overfitting 25%
• Validation Data 25%
• Learning Problems 25%
• Memetic Approach 25%
• Optimization Criterion 25%
• Numerics 25%
• Batch Learning 25%
• Takagi-Sugeno Fuzzy Systems 25%
• Nonlinear Modeling 25%
• Multi-objectivization 25%
• Data-driven Methods 25%
• Gradient-based Optimization 25%
• Learning Engine 25%
• Second Optimization 25%
• Direct Gradients 25%

Computer Science

• Fuzzy Modeling 100%
• Prediction Model 100%
• Friction Coefficient 66%
• Genetic Programming 33%
• Linear Modeling 33%
• Nonlinear Regression Model 33%
• Learning Scheme 33%
• Structure Model 33%
• Regression Method 33%
• Takagi-Sugeno fuzzy system 33%
• Learning Problem 33%
• Consequent Rule 33%
• Optimization Criterion 33%
• Component Model 33%
• Numeric Constant 33%

Chemical Engineering

• Fuzzy System 100%