MATLAB Code Implementation for Neural Network Toolbox

The MATLAB Neural Network Toolbox provides users with a convenient environment for building and training neural networks, while also supporting other machine learning algorithms such as Support Vector Machines (SVM). Below is an analysis of its core functionalities and implementation approaches:

### 1. Neural Network Construction MATLAB's Neural Network Toolbox supports various network architectures including feedforward networks, convolutional neural networks (CNN), and recurrent neural networks (RNN). Users can define network layers either through graphical interfaces (like `nntool`) or programmatically using layer functions such as `fullyConnectedLayer` and `convolution2dLayer`. The `trainNetwork` function handles model training, while data preprocessing (normalization, splitting) can be implemented using functions like `mapminmax` for feature scaling or `cvpartition` for dataset partitioning.

### 2. Support Vector Machine (SVM) Integration Although SVM is typically categorized under statistical learning, MATLAB integrates it through the `Classification Learner` App or the `fitcsvm` function for code-based implementation. The toolbox allows customization of kernel functions (linear, RBF, etc.) and hyperparameter optimization through cross-validation using the `crossval` function.

### 3. Training and Evaluation The training process supports automatic differentiation and GPU acceleration. Users can configure training parameters through `trainingOptions`, including optimizer selection (e.g., Adam), learning rate scheduling, and early stopping strategies. For evaluation, `confusionmat` calculates classification accuracy, while `plotroc` generates ROC curves for performance visualization.

### 4. Application Scenarios Extension The toolbox is suitable for time series prediction (using `narnet` for nonlinear autoregressive networks) and image classification (when combined with the Image Processing Toolbox). For complex tasks, functionality can be extended by creating custom layers through the `nnet.layer.Layer` class interface.

By appropriately combining these modules, users can efficiently implement complete workflows from data preparation to model deployment.