MATLAB-Based Function Normalization for Zero Mean and Unit Variance

This MATLAB-based function normalization tool performs data standardization by transforming input functions to have zero mean and unit variance (z-score normalization). The implementation typically involves calculating the mean and standard deviation of the input data using MATLAB's built-in functions like mean() and std(), followed by applying the transformation formula: normalized_data = (original_data - mean_value) / standard_deviation. This functionality is particularly valuable for data preprocessing as it enables more accurate comparisons between different functions by eliminating scale differences. Through normalization, users can better analyze function distributions, identify similarities and differences between datasets, and improve the performance of downstream algorithms. The normalization process is implemented through efficient vectorized operations in MATLAB, ensuring optimal computational performance. Key steps include data validation, statistical computation, and transformation application, with error handling for invalid inputs. This feature finds significant applications in data analysis, machine learning preprocessing, and statistical modeling where standardized data inputs are crucial for algorithm performance and interpretability.