Hopfield神经网络的改进及其在无线通信优化中的应用
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摘要
智能优化算法已随着时代的需求逐渐成为一个成熟的演化计算方法。Hopfield神经网络就作为典型的并行智能优化算法,以其独特的算法优越性而得到了几十年之久的长足发展,现已经成为一种理论与应用研究都较成熟完备的智能优化算法。
本论文的主要贡献和创新点如下：
介绍了Hopfield神经网络的原理,并针对其在应用中所呈现出的缺陷与不足进行了改进,并将改进后的HNN优化算法应用到通信领域中一些优化问题的解决上。本课题主要提出了三种Hopfield(?)经网络的改进算法,分别为动态步长HNN、模拟退火HNN与动态步长混沌HNN算法。本课题还将改进后的Hopfield神经网络用于解决经典组合优化问题——旅行商问题、认知无线电中最佳频谱资源利用问题以及最大化无线传感器网络的生命周期问题。
研究了在认知多载波网络中子载波功率分配算法,并结合Overlay与Underlay两种模型各自的优缺点,提出了一种混合频谱接入算法。本文针对HNN易陷入局部极值的问题,借鉴模拟退火算法的原理,提出了模拟退火-Hopfield神经网络,并将该优化算法应用于实现认知无线电系统的遍历容量最大化问题中。实验结果表明模拟退火-Hopfield神经网络可有效跳出局部极值,提高了取得全局最优解的概率,同时实验表明,混合频谱接入算法可比传统算法取得更好的遍历容量。
提出了基于tent映射和动态步长的Hopfield神经网络。用TSP问题验证了此算法的性能优于传统HNN。本文还实现了对传统LEACH分簇协议的改进算法,实现了最优簇头选择与网络生命周期最大化。结果证明此优化算法可以使得簇头的分布更加均匀,并有效延长网络的生命周期,延缓了节点的死亡时间。
In recent years, Intelligent Optimization Algorithms has become a mature intelligent optimization algorithm, more and more researchers to join in this field. Hopfield neural networks as a typical intelligent optimization algorithms has been rapid developed because its unique advantage. Now it has been an all aspects relatively complete intelligent optimization technology.
This paper introduces the theory of Hopfield neural networks briefly, aim at the shortage in the using of the Hopfield neural networks we proposed many improved algorithms, and then give some applications in the field of communication.
The main contributions of this dissertation are as follows:
This paper mainly proposed three improved Hopfield neural networks algorithms, which are dynamic step Hopfield neural networks、simulated annealing Hopfield neural networks and dynamic step chaotic Hopfield neural networks. They are effectively in high speed calculating for optimization problems especially for Non-Polynomial problems, and these improved algorithms not only increase the rate of convergence but also improve the veracity of the optimal solutions. This paper also use the improved HNN to solve traveling salesman problem、optimization spectrum using problem in cognitive radio and prolonging the lifetime of the wireless sensor networks.
We solve the optimization power allocation problem based on cognitive radio network system. We propose a Hybrid Spectrum Access (HSA) method which considers the total transmit power constraint, the peak power constraint and the primary users'tolerance. In order to solve this combinational optimization problem and achieve the global optimal solution, we derived a Simulated Annealing-Hopfield neural networks (SA-HNN). The simulation results of the optimized ergodic capacity shows that the proposed optimization problem can be solved more efficiently and better by SA-HNN than HNN or Simulated Annealing (SA), and the proposed HSA method by SA-HNN can achieve a better ergodic capacity than the traditional methods.
In wireless sensor networks (WSNs), making use of the energy efficiently is becoming increasingly important, we improves the well known cluster-based LEACH(Low-Energy Adaptive Clustering Hierarchy) protocol by defining a new cost function, which aims to minimize the intra-cluster distance and the energy consumption of the network. Moreover, this paper aims at the local optimization and the convergent rate problem of the Hopfield neural networks(HNN), and proposes an improved HNN which is based on dynamic step and tent map chaotic search algorithm and we test the performance of this network by using the TSP. Thus, we solve the optimization protocol by the improved HNN. Our protocol is compared with the traditional LEACH, simulation results demonstrate that the proposed protocol can achieve longer network lifetime.
In the end we summarize the characteristics of the application of the algorithm and give future research trends of the Hopfield neural networks.
本论文的主要贡献和创新点如下：
介绍了Hopfield神经网络的原理,并针对其在应用中所呈现出的缺陷与不足进行了改进,并将改进后的HNN优化算法应用到通信领域中一些优化问题的解决上。本课题主要提出了三种Hopfield(?)经网络的改进算法,分别为动态步长HNN、模拟退火HNN与动态步长混沌HNN算法。本课题还将改进后的Hopfield神经网络用于解决经典组合优化问题——旅行商问题、认知无线电中最佳频谱资源利用问题以及最大化无线传感器网络的生命周期问题。
研究了在认知多载波网络中子载波功率分配算法,并结合Overlay与Underlay两种模型各自的优缺点,提出了一种混合频谱接入算法。本文针对HNN易陷入局部极值的问题,借鉴模拟退火算法的原理,提出了模拟退火-Hopfield神经网络,并将该优化算法应用于实现认知无线电系统的遍历容量最大化问题中。实验结果表明模拟退火-Hopfield神经网络可有效跳出局部极值,提高了取得全局最优解的概率,同时实验表明,混合频谱接入算法可比传统算法取得更好的遍历容量。
提出了基于tent映射和动态步长的Hopfield神经网络。用TSP问题验证了此算法的性能优于传统HNN。本文还实现了对传统LEACH分簇协议的改进算法,实现了最优簇头选择与网络生命周期最大化。结果证明此优化算法可以使得簇头的分布更加均匀,并有效延长网络的生命周期,延缓了节点的死亡时间。
In recent years, Intelligent Optimization Algorithms has become a mature intelligent optimization algorithm, more and more researchers to join in this field. Hopfield neural networks as a typical intelligent optimization algorithms has been rapid developed because its unique advantage. Now it has been an all aspects relatively complete intelligent optimization technology.
This paper introduces the theory of Hopfield neural networks briefly, aim at the shortage in the using of the Hopfield neural networks we proposed many improved algorithms, and then give some applications in the field of communication.
The main contributions of this dissertation are as follows:
This paper mainly proposed three improved Hopfield neural networks algorithms, which are dynamic step Hopfield neural networks、simulated annealing Hopfield neural networks and dynamic step chaotic Hopfield neural networks. They are effectively in high speed calculating for optimization problems especially for Non-Polynomial problems, and these improved algorithms not only increase the rate of convergence but also improve the veracity of the optimal solutions. This paper also use the improved HNN to solve traveling salesman problem、optimization spectrum using problem in cognitive radio and prolonging the lifetime of the wireless sensor networks.
We solve the optimization power allocation problem based on cognitive radio network system. We propose a Hybrid Spectrum Access (HSA) method which considers the total transmit power constraint, the peak power constraint and the primary users'tolerance. In order to solve this combinational optimization problem and achieve the global optimal solution, we derived a Simulated Annealing-Hopfield neural networks (SA-HNN). The simulation results of the optimized ergodic capacity shows that the proposed optimization problem can be solved more efficiently and better by SA-HNN than HNN or Simulated Annealing (SA), and the proposed HSA method by SA-HNN can achieve a better ergodic capacity than the traditional methods.
In wireless sensor networks (WSNs), making use of the energy efficiently is becoming increasingly important, we improves the well known cluster-based LEACH(Low-Energy Adaptive Clustering Hierarchy) protocol by defining a new cost function, which aims to minimize the intra-cluster distance and the energy consumption of the network. Moreover, this paper aims at the local optimization and the convergent rate problem of the Hopfield neural networks(HNN), and proposes an improved HNN which is based on dynamic step and tent map chaotic search algorithm and we test the performance of this network by using the TSP. Thus, we solve the optimization protocol by the improved HNN. Our protocol is compared with the traditional LEACH, simulation results demonstrate that the proposed protocol can achieve longer network lifetime.
In the end we summarize the characteristics of the application of the algorithm and give future research trends of the Hopfield neural networks.
引文
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[3]J. Hopfield and D. Tank "Neural computation of decision optimization problem", Bio. Cyber., vol.53, pp.141-1521985.
[4]E. G. Larsson, M. Skoglund, "Cognitive radio in a frequency planned environment: some basic limits", to appear in IEEE Transactions on Wireless Communications, 2008.
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[6]N. Devroye, P. Mitran, V. Tarokh, "Limits on Communications in a Cognitive Radio Channel", IEEE Communications Magazine, vol.44 no 6, pp.44-49, June 2006.
[7]Tian D, Georganas N. A Coverage Preserving Node Scheduling Scheme for Large Wireless Sensor Networks [A]. The 1st ACM International Workshop on Wireless Sensor Networks and Applications [C].2002:32-41.
[8]Sun Limin and so on, Wireless sensor network, The Tsinghua University press [M], Beijing, pp.68-73,April 2005.
[9]I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "Wireless sensor networks:A survey" Elsevier Computer Networks, vol.38(4), Dec.2002, pp. 393-422.
[10]Park, Dong-Chul, Keum, Kyo-Reen, "A shortest path routing algorithm using Hopfield neural network with an improved energy function" International Journal of General Systems", v 38, n 7, p 777-791, October 2009.
[11]Yulong Liu, Mingyan Jiang, Dongfeng Yuan, "Cross-layer Resource Allocation Optimization by HNN in OFDMA-Based Wireless Mesh Networks" ICNC'09(IEEE) Volume:1; 2009 Page(s):119-123.
[12]Sheikhan, Mansour, Hemmati, Ehsan, "PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks" International Journal of Computational Intelligence Systems, v 5, n 3, p 568-581, June 2012.
[13]J. Mitola, and G. Maguire, "Cognitive radio:Making software radios more personal," IEEE Pers. Commun., vol.6, no.4, pp.13-18, Aug.1999.
[14]S. Haykin, "Cognitive radio:Brain-empowered wireless communications," IEEE J. Select. Areas Commun., vol.23, no.2, pp.201-220, Feb.2005.
[15]A. Ghasemi, and E. S. Sousa, "Fundamental limits of spectrum-sharingin fading environments," IEEE Trans. Wireless Commun., vol.6, no.2,pp.649-658, Feb. 2007.
[16]Mhatre V, Rosenberg C, Kofinan D, Mazumdar R, Shroff N. A Minimum Cost Heterogenous Sensor Network with a Lifetime Constraint[J]. IEEE Transactions on Mobile Computing,2005,4(1):4-15.
[17]Younis O and Fahmy S. HEED:A Hybrid,Energy-efficient,Distributed Clustering Approach for Ad Hoc Sensor Networks[J]. IEEE Transactions on Mobile Computering,2004,3(4):660-669.
[18]朱海燕.多频道技术在无线传感器网络中的应用[D].南京邮电大学学位论文,2009.
[19]闻新,周露,王丹力,熊晓英.MATLAB神经网络应用设计[M].科学出版社2000.
[20]王伟.人工神经网络原理—入门与应用[M].北京航空航天大学出版社1995.10.
[21]高隽.人工神经网络原理及仿真实例[M].机械工业出版社,2003.8.
[22]Abhijit S. Pandya, Robert B. Macy;徐勇,荆涛译.神经网络模式识别及其实现[M].电子工业出版社1999.6.
[23]刘希玉,刘弘.人工神经网络与微粒群优化[M].北京邮电大学出版社2008.03.
[24]Yamashita, K.; Ohta, M.; Jiang, W.; Reducing peak-to-average power ratio of multicarrier modulation by Hopfield neural network[J]. Electronics Letters Volume:38, Issue:22 2002, pp.1370-1371.
[25]Luis G. Morelli, Guillermo Abramson, and Marcelo N. Kuperman. Associative memory on a small-world neural network[C]. Eur. Phys. J. B,2004,38:495-500.
[26]Hopfield,J.J. Neural networks and physical systems with emergent collective computational abilities[C]. Proceedings of the National Academy of Sciences,USA, vol.79, pp.2554-2558.
[27]Pedro M. Talavan, Javier Yanez, "Parameter setting of the Hopfield network applied to TSP", Original Research Article Neural Networks, Vol.15, Issue 3, April 2002, Pages 363-373.
[28]魏海坤.神经网络结构设计的理论与方法[M].国防工业出版社2005.
[29]Hopfield,J.J. Neurons with graded response have collective computational properties like those of two-state neurons[C]. Proceedings of the National Academy of Sciences,USA,vol.81,pp.3088-3092.
[30]焦李成.神经网络计算[M].西安电子科技大学出版社1995.
[31]陈国良.神经计算及其在组合优化中的应用[M].计算机研究与发展1992.
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