Intelligent Optimization Algorithm: Particle Swarm Optimization (PSO) Applied to Neural Network Optimization Programs
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Resource Overview
Intelligent Optimization Algorithm: Particle Swarm Optimization (PSO) implemented for neural network optimization. Features configurations with no hidden layer, one hidden layer, and two hidden layers. Execute DemoTrainPSO.m to run the demonstration. The program includes swarm initialization, velocity updates, and fitness evaluation using neural network error metrics. Code origin: Brian Birge NCSU.
Detailed Documentation
Intelligent Optimization Algorithm: Particle Swarm Optimization (PSO) is implemented in neural network optimization programs to enhance performance. The program offers three architectural configurations: no hidden layer, one hidden layer, and two hidden layers, allowing users to select the appropriate version based on requirements. Key implementation features include particle position updates representing neural network weights, global/local best tracking for convergence, and mean squared error minimization as the fitness function. For further details on PSO applications in neural network optimization, refer to related research by Brian Birge NCSU.
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