MATLAB Implementation of CMAC Neural Network with Code Description

The step-by-step process for implementing CMAC neural network code in MATLAB is as follows: 1. Import required libraries and modules: Include essential MATLAB toolboxes and define necessary functions for neural network operations. 2. Define neural network structure and parameters: Configure CMAC-specific parameters including receptive field size, generalization parameters, learning rate, and weight initialization using functions like rand() or zeros(). 3. Prepare training and testing data: Normalize input data using mapminmax function and create proper data structures for batch processing or online learning scenarios. 4. Implement forward propagation algorithm: Calculate network output through address mapping and weight summation operations, utilizing MATLAB's matrix operations for efficient computation. 5. Implement backpropagation algorithm: Update network weights and biases using CMAC's specialized learning rule with functions tracking error gradients and applying weight adjustments. 6. Perform iterative training: Use while or for loops to conduct multiple training epochs until network convergence (monitoring mean squared error) or reaching preset iteration limits. 7. Evaluate network performance with test data: Calculate performance metrics like RMSE (Root Mean Square Error) using MATLAB's built-in functions and generate performance plots. 8. Adjust and optimize parameters: Fine-tune learning rates, receptive field parameters, and regularization techniques to improve accuracy and generalization capability. 9. Save trained network model: Use save function to store final weights and configuration parameters for future deployment and inference tasks. These steps provide the fundamental framework for developing CMAC neural network code in MATLAB, incorporating proper algorithm implementation and code optimization techniques for effective neural network development.