Adaptive Dynamic Programming (ADHDP) Implementation with Neural Network Architecture

The Adaptive Dynamic Planning (ADHDP) program employs a 3-65-1 neural network architecture, with model parameters stored in net412000.mat. This MATLAB-based implementation addresses complex optimization problems through adaptive learning algorithms that dynamically adjust solution strategies for enhanced performance. The network configuration (3-65-1) specifies three input neurons, a single hidden layer with 65 neurons, and one output neuron - a structure optimized for balancing computational efficiency and model capacity. The program incorporates key functionalities including parameter tuning mechanisms through MATLAB's neural network toolbox, allowing customization for diverse problem types. A preprocessing module handles input data normalization and noise reduction using MATLAB's filter functions and statistical operations. The architecture supports incremental learning capabilities via MATLAB's adapt function, enabling model refinement through additional training data. Critical implementation aspects include: backpropagation through time (BPTT) for temporal credit assignment, Q-learning reinforcement mechanisms implemented via reward-prediction networks, and gradient descent optimization with adaptive learning rates. The model employs MATLAB's train function with Levenberg-Marquardt optimization for rapid convergence, while maintaining compatibility with Simulink for control system applications. This ADHDP implementation serves as a robust framework for solving complex optimization challenges in domains ranging from autonomous control systems to resource allocation problems, featuring modular code structure that facilitates integration with existing MATLAB workflows and custom algorithm extensions.