An immune memory clonal algorithm for numerical and combinatorial optimization

Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm named the immune memory clonal algorithm (IMCA). The clonal operator, inspired by the immune system, is discussed first. The IMCA includes two versions based on different immune memory mechanisms; they are the adaptive immune memory clonal algorithm (AIMCA) and the immune memory clonal strategy (IMCS). In the AIMCA, the mutation rate and memory unit size of each antibody is adjusted dynamically. The IMCS realizes the evolution of both the antibody population and the memory unit at the same time. By using the clonal selection operator, global searching is effectively combined with local searching. According to the antibody-antibody (Ab-Ab) affinity and the antibody-antigen (Ab-Ag) affinity, The IMCA can adaptively allocate the scale of the memory units and the antibody population. In the experiments, 18 multimodal functions ranging in dimensionality from two, to one thousand and combinatorial optimization problems such as the traveling salesman and knapsack problems (KPs) are used to validate the performance of the IMCA. The computational cost per iteration is presented. Experimental results show that the IMCA has a high convergence speed and a strong ability in enhancing the diversity of the population and avoiding premature convergence to some degree. Theoretical roof is provided that the IMCA is convergent with probability 1.     

Multi-goal Q-learning of cooperative teams

This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section 3.1. Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm.     

Pricing decisions with retail competition in a fuzzy closed-loop supply chain

The optimal pricing decision problem of a fuzzy closed-loop supply chain with retail competition is considered in this paper. The fuzziness is associated with the customer demands, the remanufacturing cost and the collecting cost. By using game theory and fuzzy theory, the optimal decision on wholesale price, retail prices and remanufacturing rate are explored respectively under the centralized and the decentralized decision scenarios, and the expressions for them are also established. Some insights into the economic behavior of firms are given, which can serve as the basis for further study in the future.     

Terahertz spectroscopic uncertainty analysis for explosive mixture components determination using multi-objective micro-genetic algorithm

In practical applications, many suspicious samples may be a kind of mixture and consist of various chemical components that make the spectral analysis difficult. Various explosives and related compounds (ERC) in the mixture can be identified and the concentration of each component can be estimated based on the known spectral data of the pure explosive components. In this paper, the terahertz spectroscopic uncertainty analysis using a micro-GA has been proposed, in which the random assignment of alleles from parents to offspring is implied. An intelligent computation-based technical road-map is also provided for the analysis and optimisation of the terahertz spectroscopic combination analysis. A simulation with two given test cases for the ERC has been devised. The results of the simulation show that micro-GA and its derivatives have the potential applications in the fields of security, medicine and food industry to fast identify mixtures.     

Adaptive neural dynamic surface control for servo systems with unknown dead-zone

An adaptive neural controller is proposed for nonlinear systems with a nonlinear dead-zone and multiple time-delays. The often used inverse model compensation approach is avoided by representing the dead-zone as a time-varying system. The “explosion of complexity” in the backstepping synthesis is eliminated in terms of the dynamic surface control (DSC) technique. A novel high-order neural network (HONN) with only a scalar weight parameter is developed to account for unknown nonlinearities. The control singularity and some restrictive requirements on the system are circumvented. Simulations and experiments for a turntable servo system with permanent-magnet synchronous motor (PMSM) are provided to verify the reliability and effectiveness.     

基于DMSP/OLS影像的城市化水平遥感估算方法

提出了一种利用DMSP/OLS夜间稳定灯光影像定量估算区域城市化水平的方法。首先,从美国国防气象卫星计划的线性扫描业务系统(Defense Meteorological Satellite Program/Operational Linescan System,DM-SP/OLS)获取的夜间稳定灯光数据中提取了夜间灯光综合指数(Night Light Compositive Index,NLCI)。然后建立了基于社会经济统计数据的城市化水平指数(Urbanization Level Index,ULI)与NLCI间的统计学模型,并据此对中国大陆地区级尺度的城市化水平进行了估算。结果表明,ULI与NLCI间存在明显的线性关系,NLCI能较好地反映城市化水平,根据该模型得到的其他年份ULI估算值与基于统计数据的ULI实际值具有较强的一致性,故模型具有一定的可靠性和普适性。本文的创新之处在于对前人提出的NLCI公式进行了改进,并提出了公式中最优参数的搜寻算法。     

WSN中基于分簇的实时传输算法

采用跨层技术,提出一种无线传感器网络(WSN)中基于分簇的实时传输算法。在WSN中找出簇头到基站的多条优化路径,采用基于平均队列长度的拥塞控制方法,在这些路径中找出一条耗能最小且实时性最优的路径,并沿着该路径将数据传送到基站。仿真分析表明,该算法能满足网络的实时性需求,并能有效进行拥塞控制。     

基于仿射投影算法的信道均衡器研究

为了克服信号传输中码间干扰影响,改善通信质量,研究了基于仿射投影算法(APA)的信道均衡系统.设计APA均衡器的关键是确定步长因子和滤波器长度,使系统的误码率最低.首先根据其性能仿真曲线可以得到一组最优的参数值,然后构建SIMULINK仿真平台进行验证,并和LMS及NLMS均衡器作对比.结果表明APA均衡器是有效的,并能在信道失真较严重的场合保持一定的性能.     

基于微分平滑滤波的特征点跟踪

光流跟踪作为一种重要的二维运动估计技术,在运动目标检测和跟踪中有着重要的作用.L-K光流跟踪算法是一种利用Newton-Raphson梯度下降法进行图像匹配的算法,所以离散图像的偏导数的计算显得尤为重要.由于数字图像离散的方格结构导致在计算各阶偏导数时产生较大的误差.利用微分平滑滤波的方法先利用一个连续的基础函数模拟离...