An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

自组织映射法在机械故障诊断中的应用

自组织映射法是由Ｔ．Ｋｏｈｏｎｅｎ提出的一种人工神经网络模式,它能把高维的信息数据以有序方式映射到低维的网络上,形成一种拓扑意义上的有序图。由于其有序性,使得信号空间图成为许多输入信息及其关系的群落图。每个图可用灰度深浅不等的方块阴影图表示出来,这种可视的图形表示了机械运行的状态。人们可以非常直观地从图上判断机械运行状态,从而使得机械运行状态的监测和故障诊断更为简便。     

自组织聚类的P2P语义路由算法

为解决结构化P2P语义路由过程效率低的问题,通过分析哈希函数,归纳出目的节点、传统语义路由中继节点序列、聚类邻居节点集三者之间的逻辑关联特性,提出一种自组织聚类的P2P语义路由算法,在提高路由效率的同时,保证P2P网络的对等性、自组织性及规模可缩放性。     

适于高维空间搜索的自组织学习算法

提出了一种有别于当前优化算法框架的自组织学习算法(self-organizing learning algorithm,SLA),该算法融合遗传算法并行搜索与模拟退火串行搜索,结合粒子群学习机制和禁忌搜索机制,实现了系统与环境的交互学习,能够很好地处理传统优化方无法应对的高维非线性优化问题.SLA分自学习和互学习两个智能化学习阶段,先进行基于自学习机制的邻域禁忌搜索,保证局部极值的收敛,然后通过信息共享平台,进行基于互学习机制的广域禁忌搜索,保证全局极值的收敛.系统通过与环境交互学习而自适应地调整搜索策略和相关参数,使得搜索过程能够有效地避免盲目性,而具有相当的自组织性.最后,通过高维测试函数的对比仿真实验表明,SLA在由小型低维空间转入超大型高维空间时,仍能够与环境保持稳定,透明的交互学习,其全局搜索能力和整体稳健性明显优于其它搜索方法.     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.     

Experimental Investigation and Numerical Simulation of an Expanding Multifront Detonation Wave

Results of experimental investigations of an expanding multifront detonation wave are presented. Two stages of spontaneous formation of new disturbances and transverse waves on the expanding detonationwave front are observed. The main mechanisms of reinitiation of detonation waves are discussed. Twodimensional numerical simulation of the dynamics of a multifront detonation wave in a linearly expanding channel is performed. The effect of spontaneous formation of new disturbances and new transverse waves is confirmed by computations, and the main mechanism of multiplication of transverse waves is the instability of detonationwavefront elements at the stage they cease to be in the overdriven state and are attenuated during expansion.     

Tracking the events in the coverage of wireless sensor networks based on artificial neural-networks algorithms

Sensor deployment is an important problem in mobile wireless sensor networks. This paper presents a distributed self-spreading deployment algorithm (SOMDA) for mobile sensors based on artificial neural-networks self-organizing maps algorithm. During the deployment, the nodes compete to track the event and cooperate to form an ordered topology. After going through the algorithm, the statistical distribution of the nodes approaches that of the events in the interest area. The performance of the algorithm is evaluated by the covered percentage of region/events, the detecting ability and the energy equalization of the networks. The simulation results indicate that SOMDA outperforms uniform and random deployment with lossless coverage, enhancive detecting ability and significant energy equalization.     

基于自组织映射神经网络的市场清算电价预测

市场清算电价预测是电力市场中交易决策的基础。人工神经网络是电价预测较为理想的方法,但依然存在一些问题,如样本训练有时需要很长时间,存在收敛问题,特别是当样本特征量不明显的时候,这种现象更为突出。针对这一问题,利用自组织映射的聚类特性将历史数据进行特征分类和筛选处理,处理后形成的新数据用于训练三层BP神经网络,仿真结果表明,经过这种数据处理后,网络的收敛速度得到了显著提高,且预测效果良好。     

Anomaly Detection Using Real-Valued Negative Selection

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.     

基于广义细胞自动机的网络信息自组织利用方法

目前的网络信息利用模式存在着严重缺陷，它将网络上发生的海量、随机、分布、并行的信息利用行为当作是没有后效的和彼此无关的．该文提出一种新的基于网络信息自组织的信息利用模式以及基于广义细胞自动机的网络信息自组织方法．按照本文的信息利用模式，网络信息利用行为总是伴随着信息内容在网络中的扩散，网络信息利用行为成为有后效的和相关的，从而导致不同信息内容和不同内容粒度的分布式的信息自组织结构，形成基于这种信息自组织结构的网络信息利用模式．文中进而提出一种广义细胞自动机的模型、结构和算法，通过群体智能，发现网络中的信息自组织结构．分析和实验表明，基于广义细胞自动机的的网络信息自组织利用模式，在效率、自适应性和可靠性等方面优于目前的网络信息利用方法．