Source Code for Building RBF Neural Network Evaluation System

Application Approach of RBF Neural Network in Wine Quality Evaluation

In the 2012 Higher Education Cup Mathematical Modeling Competition, participants needed to solve wine quality assessment problems. Utilizing RBF neural networks to construct evaluation models proved to be an effective solution. RBF networks are particularly suitable for such evaluation tasks due to their local approximation characteristics and rapid convergence advantages.

Key Data Preprocessing Steps Feature Normalization: Since wine's physicochemical indicators have varying dimensions, data requires standardization or normalization. Common methods include Min-Max scaling and Z-score normalization, implemented using sklearn's StandardScaler or MinMaxScaler classes. Outlier Handling: Identify and process anomalous data points through boxplot analysis or 3σ principle, achievable with scipy.stats for statistical detection. Feature Correlation Analysis: Analyze correlations between physicochemical indicators using correlation coefficient matrices, removing highly correlated features through pandas DataFrame.corr() method. Data Balancing: When wine sample quantities across different grades are imbalanced, apply oversampling (SMOTE algorithm) or undersampling techniques using imbalanced-learn library.

RBF Network Construction Essentials Determining Hidden Layer Nodes: Typically use trial-and-error methods or clustering algorithms (like K-means) to automatically determine optimal center points, implemented via sklearn.cluster.KMeans. Selecting Radial Basis Functions: Gaussian function is the most common kernel function, requiring appropriate spread constant optimization using grid search in sklearn.model_selection. Output Layer Design: Employ linear output for regression or softmax classification for categorical evaluation, implemented through tensorflow.keras.layers.Dense with corresponding activations.

Model Optimization Directions Parameter Tuning: Determine optimal network parameters through cross-validation using sklearn.model_selection.GridSearchCV. Ensemble Methods: Combine multiple RBF networks through ensemble learning (bagging or boosting) to enhance robustness using sklearn.ensemble.VotingClassifier. Interpretability Enhancement: Explain the impact degree of each physicochemical indicator on evaluation results through sensitivity analysis with SHAP or LIME libraries.

Comparison with Other Methods Compared to BP neural networks, RBF networks train faster and are less prone to local optima. Versus SVM, RBF networks show relative insensitivity to parameter selection. However, note that RBF networks exhibit stronger dependency on center point selection accuracy.