Particle swarm optimisation-based support vector machine for intelligent fault diagnosis
International Journal of Computer Applications in Technology
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Construction project quality management, the basis of construction management, is crucial for construction firms to survive and grow in the industry. This paper presents the adoption of a particle swarm optimization (PSO) model to train perceptrons in assessment and predicting the quality of construction projects in China. Artificial Neural Network (ANN) has preeminent learning ability, but often exhibit inconsistent and unpredictable performance for noisy data. The Particle Swarm Optimization (PSO) technique is used to train the multi-layered feed forward neural networks to discriminate the different operating conditions. Comparing with back-propagation ANN and ANN based on genetic algorithms, the simulated results of quality assessement of construction projects show that training the neural network by PSO technique gives more accurate results (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) than BPN and GA-based ANN.