基于改进的Tent混沌万有引力搜索算法

万有引力搜索算法(gravitational search algorithm,GSA)相比于传统的优化算法具有收敛速度快、开拓性能强等特点,但GSA易陷入早熟收敛和局部最优,搜索能力较弱.为此,提出一种基于改进的Tent混沌万有引力搜索算法(gravitational search algorithm based on improved tent chaos,ITC-GSA).首先,改进Tent混沌映射来初始化种群,利用Tent混沌序列随机性、遍历性和规律性的特性使得初始种群随机性和遍历性在可行域内,具有加强算法的全局搜索能力;其次,引入引力常数G的动态调整策略提高算法的收敛速度和收敛精度;再次,设计成熟度指标判断种群成熟度,并使用Tent混沌搜索有效抑制算法早熟收敛,帮助种群跳出局部最优;最后,对10个基准函数进行仿真实验,结果表明所提算法能够有效克服GSA易陷入早熟收敛和局部最优的缺点,提高算法的收敛速度和寻优精度.     

Gravitational search algorithm based data scheduling for peer to peer video on demand system

Multimedia Tools and Applications - In peer to peer (P2P) video streaming systems, peers in network assist to forward the data to other peers without the interference of central servers. Video on...     

Nonlinear system identification and control of chemical processes using fast orthogonal search

The use of the fast orthogonal search (FOS) method is presented for model estimation and control of nonlinear chemical processes. FOS provides a nonlinear approximation used in an inner-loop that allows for simpler linear control methods to be used as an outer-loop controller. It is a straightforward, simple-to-use method for linearization of systems based on orthogonal system identification. The control concept is derived from the method of inverse dynamic control (IDC). The novel combination of this with the FOS method of system identification results in a very efficient and effective method of control. The method is demonstrated and tested on two nonlinear chemical process control simulations and the results are shown to compare very favourably to published results on the same problems.     

基于反馈策略的引力搜索算法及其在支持向量机中应用

针对标准引力搜索算法(SGSA)在高维多峰函数寻优过程中容易出现早熟的问题,提出一种基于反馈策略的引力搜索算法(FGSA)。由于粒子在进化过程中群体多样性损失过快,采用粒子与最佳位置的距离和最邻近粒子的距离两个参数来均衡优化算法的勘探和开发能力,并将变异操作引入到FGSA中。通过对选取的四个基准函数测试,验证了FGSA和SGSA相比,在高维多峰函数寻优时,精确度和稳定性都有显著提高。同时,针对支持向量机(SVM)分类问题时,可有效地找出合适的特征子集及SVM参数,并取得较好的分类结果。     

平面选址问题的引力搜索算法求解

为求解平面选址问题,给出了一种基于引力搜索算法的求解方法。算法利用万有引力定律进行全局搜索,采用一种邻域搜索方法进行局部搜索,实现算法全局优化和局部优化的平衡。通过大量实验和与现有求解方法的比较,结果验证了算法的可行性和有效性。     

基于启发式搜索的车道线识别算法研究

在车道边界识别中,边界点的提取是关键,常用的边界点提取方法因对噪声的抑制能力不强产生较多噪声点,从而影响识别效果。提出一种边界点启发式搜索算法,根据梯形匹配模型、车道线灰度变化特征和实际车道宽度约束,确定搜索的起始点,从起始点根据度量代价准则函数搜索车道边界点。采用直线道路模型结合Hough变换来拟合车道边界。实验表明,该算法实时性好、可靠性强、鲁棒性高。     

An adaptive orthogonal search algorithm for model subset selection and non-linear system identification

A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection and non-linear system identification. Model structure detection is a key step in any system identification problem. This consists of selecting significant model terms from a redundant dictionary of candidate model terms, and determining the model complexity (model length or model size). The final objective is to produce a parsimonious model that can well capture the inherent dynamics of the underlying system. In the new AOS algorithm, a modified generalized cross-validation criterion, called the adjustable prediction error sum of squares (APRESS), is introduced and incorporated into a forward orthogonal search procedure. The main advantage of the new AOS algorithm is that the mechanism is simple and the implementation is direct and easy, and more importantly it can produce efficient model subsets for most non-linear identification problems.     

一种基于用户查询行为模型的案例查询算法

传统的案例查询算法通过被动响应用户的查询请求为用户返回与查询请求相关的案例,忽略了用户查询行为能够对案例查询过程进行指导。提出了一个基于用户查询行为模型的案例查询算法,通过收集用户的查询请求,利用用户查询行为之间的相似度建立用户查询行为的分类模型;分析了用户查询行为的分类算法,重点论述了用户查询行为模型对案例查询过程的指导过程。实验结果表明,该方法能够有效地提高查询结果召回率以及查询成功率。     

基于支持向量机N4SID辨识模型的非线性预测控制

针对工业控制领域中非线性系统的模型辨识与预测控制问题,采用最小二乘支持向量机回归方法构造非线性函数,运用状态子空间（N4SID）模型辨识方法辨识非线性状态空间模型.在此基础上建立非线性预测控制器,利用拟牛顿算法进行非线性预测控制律的求解,从而实现了一种新的基于支持向量机N4SID辨识模型的非线性预测控制算法.仿真实验验证了该算法的有效性和可行性.     

Nonlinear system identification using wavelet networks

A novel identification scheme using wavelet networks is presented for nonlinear dynamical systems. Based on fixed wavelet networks, parameter adaptation laws are developed using a Lyapunov synthesis approach. This guarantees the stability of the overall identification scheme and the convergence of both the parameters and the state errors, even in the presence of modelling errors. Using the decomposition and reconstruction techniques of multiresolution decompositions, variable wavelet networks are introduced to achieve a desired estimation accuracy and a suitable sized network, and to adapt to variations of the characteristics and operating points in nonlinear systems. B-spline wavelets are used to form the wavelet networks and the identification scheme is illustrated using a simulated example.     

基于动力学特性聚类多维泰勒网的非线性系统辨识与预测

针对时变非线性系统难以建模的问题, 提出了基于动力学特性聚类的多维泰勒网模型, 对系统进行辨识与预测. 首先讨论了多维泰勒网模型构造方法和非线性系统动力学特性聚类的定义; 然后给出基于动力学特性聚类的多维泰勒网自重构算法; 最后通过实例说明基于动力学特性聚类多维泰勒网在实际中应用的方法, 实例结果验证了该方法的有效性.     

Harmony search algorithm for infinite impulse response system identification

In this paper, optimal sets of filter coefficients are searched by a meta-heuristic optimization technique called Harmony Search (HS) algorithm for infinite impulse response (IIR) system identification problem. For different optimization problems, HS algorithm undergoes three basic rules; namely Random Selection (RS), Harmony Memory Consideration (HMC), and Pitch Adjustment (PA) rules, which are inspired from the process that the musicians use to improvise a perfect state of harmony with the consummate skill of blending notes in tune. With the help of the properly selected control parameters, a perfect balance is achieved in exploration and exploitation in searching phases. The detailed analysis of simulation results emphasizes the strength of HS algorithm to find the near-global optimal solution, quality of convergence profile and the speed of convergence while tested against standard benchmark examples for same and reduced order models.     

IIR model identification using a modified inclined planes system optimization algorithm

Inclined planes system optimization (IPO) is a new optimization algorithm inspired by the sliding motion dynamic along a frictionless inclined surface. In this paper, with the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The IIR model identification is a complex and practical challenging problem due to multimodal error surface entanglement that many researches have been reported for it. In this work, MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors. To do this, unknown filter parameters are considered as a vector to be optimized. In implementation, at first, to demonstrate the effectiveness of the proposed method, 10 well-known benchmark functions have been considered for evaluating and testing. In addition, statistical analysis on the powerfulness, efficiency and applicability of the MIPO algorithm are presented. Obtained results in compared to some other popular methods, confirm the efficiency of the MIPO algorithm that makes the best optimal solutions and has a better performance and acceptable solutions.     

基于语义搜索的语音交互系统模型研究

本文探讨了基于语义搜索的语音识别,比较了文本搜索和语义搜索的差别,并构建了分布式语义模型,建立了基于语义搜索的语音识别本体库,我们的研究说明基于语义搜索的语音识别具有很大的理论价值和实际作用。     

Genetic algorithm based defect identification system

In this paper we develop a genetic algorithm based defect identification system for machined-parts inspection purposes. The system is based on genetic algorithm and knowledge system techniques.     

基于平衡学习的CMAC 神经网络非线性辨识算法

为提高小脑模型关节控制器(CMAC)神经网络在线学习的快速性和准确性，提出一种平衡学习的概念，并设计一种改进的CMAC学习算法．在常规的CMAC中，误差的校正值被平均地分配给所有激活存储单元，而不管这些存储单元的可信度；在改进的CMAC中，利用激活单元先前学习次数作为可信度，其误差校正值与激活单元先前学习次数的负k次方成比例．仿真结果表明，当k为一适当数值时，改进CMAC具有较快的学习速度和较高的精度，特别是在神经网络的初始学习阶段．     

改进回溯搜索算法解决光伏模型参数识别问题

为了准确、可靠地识别光伏模型参数,提出一种改进回溯搜索算法(MBSA).该算法首先通过选取部分种群个体同时学习当前种群和历史种群信息,而其他个体向当前种群中最优个体学习并远离最差解,从而保持种群多样性并提高收敛速度;然后,通过概率来量化总体中的个体性能,进而每个个体基于概率自适应地选择不同的进化策略来平衡探索和开发能力...     

一种基于直线模型的车道线识别算法研究*

为了更好地满足车道标志线识别算法的实时性和鲁棒性要求,提出了一种新的、有效的车道标志线识别算法。将图像灰度化后,采用中值滤波去除图像采集过程中引入的噪声,应用方向可调滤波器进行边缘提取,在提取过程中对原图像进行感兴趣区域划分并采用边缘分布函数法确定方向可调滤波器的初始方向角。提出使用基于梯度加权的霍夫变换对车道标志线进行识别,通过建立梯形感兴趣区域的方法实现对车道标志线的实时跟踪,并对多段实地采集的视频进行实验测试。结果表明:基于方向可调滤波器与梯度加权的霍夫变换相结合的车道标志线识别方法,简化了对车道标志线信息特征参量的估计;不仅大大缩减了算法的执行时间,而且使算法的鲁棒性得到很大的提高。     

Nonlinear thermal system identification using fractional Volterra series

Linear fractional differentiation models have already proven their efficacy in modeling thermal diffusive phenomena for small temperature variations involving constant thermal parameters such as thermal diffusivity and thermal conductivity. However, for large temperature variations, encountered in plasma torch or in machining in severe conditions, the thermal parameters are no longer constant, but vary along with the temperature. In such a context, thermal diffusive phenomena can no longer be modeled by linear fractional models. In this paper, a new class of nonlinear fractional models based on the Volterra series is proposed for modeling such nonlinear diffusive phenomena. More specifically, Volterra series are extended to fractional derivatives, and fractional orthogonal generating functions are used as Volterra kernels. The linear coefficients are estimated along with nonlinear fractional parameters of the Volterra kernels by nonlinear programming techniques. The fractional Volterra series are first used to identify thermal diffusion in an iron sample with data generated using the finite element method and temperature variations up to 700 K. For that purpose, the thermal properties of the iron sample have been characterized. Then, the fractional Volterra series are used to identify the thermal diffusion with experimental data obtained by injecting a heat flux generated by a 200 W laser beam in the iron sample with temperature variations of 150 K. It is shown that the identified model is always more accurate than the finite element model because it allows, in a single experiment, to take into account system uncertainties.     

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.