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一类非线性动力系统的模糊反向传播神经网络控制

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

作者信息

Sakhre Vandana, Jain Sanjeev, Sapkal Vilas S, Agarwal Dev P

机构信息

Madhav Institute of Technology & Science, Gwalior 474005, India.

SGB Amravati University, Amravati 444062, India.

出版信息

Comput Intell Neurosci. 2015;2015:719620. doi: 10.1155/2015/719620. Epub 2015 Aug 20.

DOI:10.1155/2015/719620
PMID:26366169
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4558459/
Abstract

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

摘要

针对一类非线性动力系统，开展了模糊反向传播神经网络（FCPN）控制器设计。在此过程中，分别通过模糊竞争学习（FCL）来调整输入层与隐含层、隐含层与输出层之间的连接权重。FCL范式采用学习原理来计算所提出的最佳匹配节点（BMN）。该策略为非线性动力系统提供了鲁棒控制。基于平均绝对误差（MAE）、均方误差（MSE）、最佳拟合率（BFR）等指标，将FCPN与动态网络（DN）和反向传播网络（BPN）等现有网络进行了比较。结果表明，所提出的FCPN比DN和BPN具有更好的性能。通过对四个非线性动力系统以及多输入单输出（MISO）和单输入单输出（SISO）燃气炉Box-Jenkins时间序列数据进行仿真，验证了所提出的FCPN算法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/01447fd3cdff/CIN2015-719620.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/ede14326edd9/CIN2015-719620.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/654aa4204554/CIN2015-719620.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/67f94151b9dd/CIN2015-719620.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/2a546e46176a/CIN2015-719620.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/85c197383838/CIN2015-719620.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/94d12335832b/CIN2015-719620.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/01447fd3cdff/CIN2015-719620.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/ede14326edd9/CIN2015-719620.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/654aa4204554/CIN2015-719620.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/67f94151b9dd/CIN2015-719620.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/2a546e46176a/CIN2015-719620.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/85c197383838/CIN2015-719620.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/94d12335832b/CIN2015-719620.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fa/4558459/01447fd3cdff/CIN2015-719620.008.jpg

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