A Madaline Network Learning Algorithm Based on Improved MRII

doi:10.3969/j.issn.1006-2475.2016.02.011

Abstract

Abstract: In this paper, by analyzing the Madaline network work principle, according to the network characteristic and the shortcomings of the existing algorithms including the adjust weigh formula used in MRII algorithm with many parameters, but most of which come from experience in practice without theoretical reason, it made networks fall into “local recycle” with turning over the neuron based on confidence principle. We present an improved learning algorithm based on MRII, by establishing neuron’s sensitivity as a tool for measuring the turn of each hidden neuron, which reduces the number of weight adjustment. Some experimental results verify the effectiveness of the proposed algorithm.

Key words: neural network, Madaline, learning algorithm, sensitivity, MRII

ZHENG Xing, LI Zong-xiang. A Madaline Network Learning Algorithm Based on Improved MRII[J]. Computer and Modernization, 2016, 0(2): 49-51,57.

References

［1］胡守仁. 神经网络导论［M］. 长沙:国防科技大学出版社, 1993.
［2］张立明. 人工神经网络的模型及其应用［M］. 上海:复旦大学出版社, 1993.
［3］ Winter R, Widrow B. Madaline Rule II: A training algorithm for neural networks［C］// IEEE International Conference on Neural Networks. 1988: 401-408.
［4］ Winter R. Madaline Rule II: A new method for training networks for Adalines［D］. PhD Thesis, Department of Electrical Engineering, Standford University, 1989.
［5］张军英,许进. 二进前向人工神经网络理论与应用［M］. 西安：西安电子科技大学出版社, 2001.
［6］ Wang Yingfeng, Zeng Xiaoqin, Yeung Daniel So, et al. Computation of Madalines’ sensitivity to input and weight perturbations［J］. Neural Computation, 2006,18(11):2854-2877.
［7］ Zhang Chao, Yang Jie, Wu Wei. Binary higher order neural networks for realizing Boolean functions［J］. IEEE Transactions on Neural Networks, 2011,22(5):701-703.
［8］ Zeng Xiaoqin, Yeung D S. Hidden neuron pruning of multilayer perceptrons using a quantied sensitivity measure［J］. Neurocomputing-IJON, 2006,69(7-9):825-837.
［9］ Yeung D S, Ng W W Y, Wang Defeng, et al. Localized generalization error model and its application to architecture selection for radial basis function neural network［J］. IEEE Transactions on Neural Networks, 2007,18(5):1294-1305.
［10］Zhong Shuiming, Zeng Xiaoqin, Wu shengli, et al. Sensitivity-based adapitive learning rules for binary feedforward neural networks［J］. IEEE transactions on neural networks and learning systems, 2012,23(3):480-491.
［11］Yang S, Ho C, Siu S. Computing and analyzing the sensitivity of MLP due to to the errors of the i.i.d inputs and weights based on CLT［J］. IEEE Transactions on Neural Networks, 2010,21(12):1882-1891.
［12］Zeng Xiaoqin, Wang Yingfeng, Zhang Kang. Computation of Adalines’ sensitivity to weight perturbation［J］. IEEE Transactions on Neural Networks, 2006,17(2):515-519.
［13］Beak S M, Park J W. Hessian matrix estimation in hybrid systems based on an embedded FFNN［J］. IEEE Transactions on Neural Networks, 2010,21(10):1533-1542.
［14］哈根. 神经网络设计［M］. 戴葵译. 北京：机械工业出版社, 2002.

[1]	ZENG Li-li, TANG Hua-bei, NIU Yi-xiao, MENG Fan-yue. Lithofacies Identification Method Based on LSTM Stacked Residual Network [J]. Computer and Modernization, 2023, 0(08): 38-43.
[2]	WU Hai-yun. Discrimination of Injection and Production Connection Based on MLP and Sobol [J]. Computer and Modernization, 2020, 0(03): 40-.
[3]	DONG Xue， ZHANG De-ping. Global Sensitivity Analysis and Optimization of Submarine Combat Effectiveness #br# Based on Extreme Learning Machine [J]. Computer and Modernization, 2018, 0(05): 86-.
[4]	GENG Juncheng1, ZHANG Xiaofei1, SUN Yubao2, WU Bo1, ZHOU Qiang2. Power Failure Sensitivity Prediction Algorithm Using Ksupport Sparse Logistic Regression [J]. Computer and Modernization, 2018, 0(04): 68-.
[5]	HE Yu-hui. Privacy Preservation Mechanismin of Opportunistic Networks Based on Stochastic Game [J]. Computer and Modernization, 2016, 0(9): 30-34.
[6]	FAN Peng-fei;FAN Yong-hua;YU Yun-feng. H_∞/Mixed Sensitivity Controller Design for Airlaunched Cruise Missile [J]. Computer and Modernization, 2013, 1(7): 105-108.
[7]	CHEN Kai;YING Ren-dong;LIU Pei-lin;YU Wen-xian . High Sensitivity GPS Signal Tracking Based on Viterbi Algorithm [J]. Computer and Modernization, 2013, 1(4): 171-175.
[8]	JIN Ming-ju;JIANG Hua-wei. Fusion Image Quality Evaluation Algorithm Based on Human Visual Features [J]. Computer and Modernization, 2013, 1(3): 184-188.
[9]	ZHONG Ming-xia. Solution of Reducing False-positive Rate in Medical CAD Systems [J]. Computer and Modernization, 2010, 1(10): 81-82.
[10]	ZHANG Chun-qiang;ZHANG Ming-min. Noise Receiver Sensitivity on Cylindrical Transducer of Insulation Resistance Influence [J]. Computer and Modernization, 2009, 169(9): 149-151.