Abstract
The unit feedback recursive neural network model which is widely used at present has been analyzed. It makes the unit feedback recursive neural network have the same dynamic process and time delay characteristic. The applications of the unit recursive neural networks are limited. For its shortcomings, we proposed another state feedback recursive neuron model, and their state feedback recursive neural network model. In this neural network model, the static weight of the neural network explained the static transmission performance, and the state feedback recursive factor indicated the dynamic performance of neural networks, the different state feedback recursion factor indicated the dynamic process time of the different systems. Therefore, the state-feedback neural network dynamic characteristics and learning strategies, which have great theoretical and application significance, are studied.
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References
Cong Shuang, Dai Yi (2004) Structure of recurrent neural networks. Comput Appl 24(8):18–20
Wu Yilei, Song Qing, Liu Sheng (2008) A normalized adaptive training of recurrent neural networks with augmented error gradient. IEEE Trans Neural Netw 19(2):351–356
Qing Song, Yilei Wu, Yeng Chai Soh (2008) Robust adaptive gradient-descent training algorithm for recurrent neural networks in discrete time domain. IEEE Trans Neural Netw 19(11):1841–1853
Han Mini, Shi Zhi-wei, Xi Jian-hui (2006) Learning the trajectories of periodic attractor using recurrent neural network. Control Theory Appl 23(4):497–502
Sheng-Sung Yang, Sammy Siu, Chia-Lu Ho (2008) Analysis of the initial values in split-complex backpropagation algorithm. IEEE Trans Neural Netw 19(9):1564–1573
Cong Shuang, Liang Yan-yang, Li Guo-dong (2006) Multivariable adaptive PID-like neural network controller and its design method. Info Cont 35(5):568–573
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Xiang, C., Huihuang, P. (2014). The Study on Neural Network Feedback based on Learning Algorithm. In: Zhong, S. (eds) Proceedings of the 2012 International Conference on Cybernetics and Informatics. Lecture Notes in Electrical Engineering, vol 163. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3872-4_62
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DOI: https://doi.org/10.1007/978-1-4614-3872-4_62
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