bp神经网络算法 Resources

Showing items tagged with "bp神经网络算法"

The backpropagation neural network algorithm represents one of the most advanced optimization solutions. This paper examines the widely-used feedforward neural networks and discusses key algorithmic improvements. While the error backpropagation (BP) algorithm dominates weight learning approaches, it suffers from limitations like local minima and slow convergence. The Levenberg-Marquardt optimization-based algorithm addresses some issues but neglects second-order terms. This research explores approximate Hessian matrix computation when error functions are nonlinear and second-order term S(W) becomes significant, providing enhanced network training methodology with implementation insights.

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Application Background: Digital recognition represents a crucial research direction in the pattern recognition field with broad application prospects. Based on fundamental principles of BP neural networks, this paper proposes a handwriting digit recognition solution utilizing BP neural network methodology. Key Technology: The core concept of the BP algorithm involves a learning process consisting of two phases: forward propagation of signals and backward propagation of errors. During forward propagation, input samples pass through the input layer, undergo progressive processing through hidden layers, and transmit to the output layer. If discrepancies exist between actual outputs and expected outputs (teacher signals), the system initiates the backward error propagation phase.

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MATLAB code for BP neural network algorithm that uses pre-trained network file ANN.mat to predict new data files, calculates mean squared error, and generates comparative plots between predicted and original data. Includes code explanations for data preprocessing, network loading, prediction implementation, and performance visualization.

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