Neural Network Controller Design Using Backpropagation Method
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Without relying on MATLAB toolboxes, we can implement neural network controller design using the Backpropagation (BP) method to achieve superior control performance. The implementation typically involves defining the network architecture with input, hidden, and output layers, then applying the BP algorithm for weight updates through gradient descent. Key implementation steps include forward propagation to calculate outputs, error computation between predicted and desired outputs, and backward propagation to adjust weights using chain rule derivatives. This approach allows custom initialization of network parameters, manual tuning of learning rates, and implementation of momentum terms for convergence optimization. The core algorithm iteratively minimizes the cost function through partial derivatives of weights and biases, enabling the controller to learn complex nonlinear relationships between system inputs and outputs.
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