Implementation of Artificial Neural Network Classification for Iris Dataset

In the MATLAB environment, we implement artificial neural network classification for the iris dataset using a manually programmed Backpropagation (BP) network algorithm. This implementation employs the classical backpropagation algorithm that trains the network through iterative updates of weights and biases to achieve accurate classification of iris data. The implementation follows these key steps: First, input data undergoes standardization/normalization preprocessing. Second, network weights and biases are initialized, typically using random small values. Third, forward propagation calculates each neuron's output values through weighted sums and activation functions (commonly sigmoid or tanh). Fourth, backward propagation adjusts weights and biases based on computed errors using gradient descent optimization. The algorithm iterates until meeting convergence criteria like error threshold or maximum epochs. Code implementation details include: defining network architecture (input-hidden-output layers), implementing matrix operations for efficient weight updates, and incorporating momentum terms for stable convergence. The trained network undergoes accuracy evaluation using test data, with performance metrics like confusion matrix and classification accuracy outputting final results. Although this manually implemented BP network is relatively simple compared to modern deep learning frameworks, it effectively performs iris dataset classification tasks while demonstrating fundamental neural network principles through hands-on coding practice.