基于晶格的人工内分泌系统模型及在多机器人系统中的应用

为了解决多机器人系统分布式控制问题,借鉴人体内分泌系统研究的理论成果,提出基于晶格的人工内分泌系统模型.该模型以环境晶格化为基础,以细胞智能化为依托,以累加激素为纽带,以靶细胞为导向,能够适应外环境的持续变化,保持内环境的相对稳定,充分体现人体内分泌系统的自组织和自修复特性.实验结果表明,无需复杂的集中控制策略,基于该...     

基于冯•诺依曼邻域结构的人工鱼群算法

提出了一种基于冯¢ 诺依曼邻域结构的人工鱼群算法. 每条人工鱼只和与自己相连的上下左右的人工鱼进行信息交换, 从而减少了计算邻域中心位置和极值位置的计算量, 有效地维持了种群的多样性, 加快了算法的运行速度. 在觅食行为中, 人工鱼通过直接移动到搜索到的较好位置, 来加快搜索速度. 在随机游动行为中, 人工鱼以小半径进行搜索, 因此算法的优化精度得到了提高. 采用动态调整人工鱼视野和步长的方法, 较好地平衡了全局搜索能力和局部搜索能力. 仿真和实例计算结果表明, 该算法具有更好的优化性能.     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。     

基于正交试验设计的克隆选择函数优化

将正交试验设计引入到克隆选择操作中,设计出基于正交试验的克隆选择操作(clonal selection operation based on orthogonal experiment design,简称CSO-OED),并将其加入到典型的克隆选择算法中,设计出并联式的CSO+CSO-OED(I)算法和串联式的CSO+CSO-OED(II)算法.将新设计的算法用于9个经典的测试函数和6个复杂的测试函数进行对比测试,实验结果表明,CSO-OED能够有效地保持种群的多样性,避免算法不成熟收敛.CSO+CSO-OED(I)和CSO+CSO-OED(II)将全局搜索和局部搜索分开进行优化,对比实验表明,这种搜索策略不但能够保证算法的收敛性,还能有效地提高搜索解的精度,增强算法的鲁棒性.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

基于优化目标可调控的免疫网络算法的分析与应用

针对目前几种免疫网络模型在数据聚类方面应用的一些不足,在aiNet免疫算法的基础上结合函数优化的思想提出一种基于目标可调控的免疫模型。并在算法中给出目标控制函数,和细胞记忆库的概念。本算法提高了免疫学习质量并从整体上对免疫网络进行优化。     

T–S fuzzy model-based tracking control of a one-dimensional manipulator actuated by pneumatic artificial muscles

Pneumatic artificial muscle (PAM) has highly nonlinear and time-varying behavior due to gas compression and nonlinear elasticity of the bladder containers. Hence, it is difficult to achieve excellent tracking performance when using classical control methods. This study proposes a Takagi–Sugeno (T–S) fuzzy model-based control for improving control performance. The proposed approach decomposes the model of a nonlinear system into a set of linear subsystems. This allows, the T–S fuzzy model-based controller to use simple linear control techniques providing a systematic framework for the design of a state feedback controller. Stability analysis is carried out using Lyapunov direct method. The powerful LMI Toolbox in MATLAB is employed to solve linear matrix inequalities (LMIs) to obtain the controller gains. Experimental results verified that the proposed controller can achieve excellent tracking performance under different disturbances.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

基于多代理技术的入侵检测机制研究

本文针对传统入侵检测技术难以适应无线传感器网络的动态性和资源有限等特点的问题,将人工免疫原理和多代理技术相结合,提出了一种适用于分簇式无线传感器网络的入侵检测机制(IMAIDM);给出了免疫Multi-Agent模型描述、Multi-Agent的功能定义以及相关算法。     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.