摘要
Layered feedforward neural network training algorithm based on traditional BP algorithm may lead to entrapment in local optimum, and has the defects such as slow convergent speed and unsatis-fied dynamic character which reduce the study ability of the network. This paper presents an improved adaptive genetic algorithm (IAGA) for training the neural network efficiently that uses a forward adaptive technique and takes the advantages of the network architecture. The experimental results show that our al-gorithm outperforms BP algorithm, BGA algorithm and AGA algorithm, and the dynamic character,training accuracy and efficiency proved greatly.
Layered feedforward neural network training algorithm based on traditional BP algorithm may lead to entrapment in local optimum, and has the defects such as slow convergent speed and unsatis-fied dynamic character which reduce the study ability of the network. This paper presents an improved adaptive genetic algorithm (IAGA) for training the neural network efficiently that uses a forward adaptive technique and takes the advantages of the network architecture. The experimental results show that our al-gorithm outperforms BP algorithm, BGA algorithm and AGA algorithm, and the dynamic character,training accuracy and efficiency proved greatly.
