Pattern Classification Application Using BP Learning Algorithm

This implementation utilizes the momentum backpropagation (BP) learning algorithm to classify the classic UCI Iris plant dataset, delivering both rapid processing speed and high classification accuracy. The iris.arff file represents the original database file that can be examined using Weka data mining software. For data input in our implementation, we provide the Iris.csv source file, which can be converted to the required format using Weka software. The algorithm employs key MATLAB functions including data preprocessing, network initialization with momentum parameters, forward propagation with sigmoid activation functions, error backpropagation with weight updates, and convergence checking. The momentum term helps accelerate convergence and avoid local minima by incorporating previous weight update directions. To execute the classification algorithm, simply place the source file Iris_classify.m and Iris.csv in MATLAB's work directory and run the main script. The implementation features automatic data loading, network training with configurable learning rates and momentum factors, and performance evaluation through confusion matrix analysis. This user-friendly algorithm provides a practical demonstration of neural network pattern classification and we hope it contributes value to your research efforts!