基于SAIL模型的HJ-1卫星LAI反演算法研究

基于辐射传输模型和人工神经网络算法,研究出适用于环境一号卫星CCD相机数据的叶面积指数反演算法针对环境一号卫星CCD相机的波段特征,设计出一种新的植被指数HJVI,有效的避免了数据饱和现象,提高了算法的精度。     

改进的遗传算法在污水管网中的应用

城市污水管网中各级泵站密切相关,泵站间由于链级制抑而协调性差,导致整个管网运行效率不高,甚至引起污水外溢。因此,本文提出了一种改进式遗传算法并应用于管网中各级泵站节点,从而使管网流入流出平衡,链级效应消失,整个污水排放系统处于最佳状态,同时降低了各级泵站能耗,提升了管网的运行效率。     

医用充气式保温毯控制系统设计及仿真

针对医用充气式保温毯的温度控制要求,提出其温度控制闭环系统的结构,系统的硬件电路主要由温度传感器、单片机及RS232通讯模块等组成。采用PWM方法控制晶闸管导通时间的方式来改变加热丝的加热时间、采用自适应模糊PID控制方法实现保温毯气囊出气温度的自动调节与控制。将整个控制过程分为两个阶段:自动调节初始阶段,加热丝加热的占空比为100%;当温度达到一定值后转入自适应模糊PID控制阶段,由相应控制量来决定加热丝加热的占空比,实现气体温度的调节。通过MATLAB仿真分析表明,所采用的自适应模糊PID控制方法优于常规的PID控制,具有良好的适应性和鲁棒性,可明显提高系统的稳态精度。     

多目标优化问题的连续域蚁群算法仿真研究

本文研究了一种用于求解带有多个约束条件(multi-objective optimization problem,MOP)的连续域蚁群算法。该算法定义了连续域中信息量的留存方式和蚂蚁的行走策略,并将信息素交流和基于全局最优经验指导两种寻优方式相结合,将当前发现的所有的非支配解保存起来,进而用这些解来指导蚂蚁朝着散布较为稀疏的区域寻优,以保证解的分布性能,并提高了蚁群算法的收敛速度,同时维持了群体的多样性。     

基于蚂蚁算法的制造资源搜索调度研究

本文基于对制造资源调度和发现过程的分析研究,建立实用的制造资源调度器模型和调度策略,然后采用蚂蚁算法对制造资源的调度快表做出优化,并做出有关的实验和数据分析。该方法能够有效地实现制造资源的作业的合理调度和实现分布式系统的负载平衡,提高了制造资源搜索的成功率。     

悬液细胞图象识别的一种有效新算法

为进一步提高细胞识别的速度和识别率,本文研究一种细胞识别新算法。不同于其它的只对整个细胞形态进行一次图象处理的细胞识别方法,该算法在图象处理过程中,先后以细胞形态为重点和以细胞核形态为重点对原始图象进行两次图象处理,分别得到图象清晰度很高的整个细胞的二值图和细胞核的二值图。然后提取各项特征参数,对细胞图象进行识别。最后应用该算法对多幅细胞图象进行识别,结果表明该算法不但识别速度快,平均每幅图象的时间在3秒左右,而且识别率高达85%以上。     

Robust fuzzy stabilization of dithered chaotic systems using island-based random optimization algorithm

Applying dither to highly nonlinear systems may suppress chaotic phenomena, but dynamic performance, such as convergence rate and disturbance attenuation, is usually not guaranteed. This paper presents a dithered H_∞ robust fuzzy control scheme to stabilize chaotic systems that ensures disturbance attenuation bounds. In the proposed scheme, Takagi-Sugeno (T-S) fuzzy linear models are used to describe the relaxed models of the dithered chaotic system, and fuzzy controllers are designed based on an extension to the concept of parallel distributed compensation (PDC). Sufficient condition for the existence of the H_∞ robust fuzzy controllers is presented in terms of a novel linear matrix inequalities (LMI) form which takes full consideration of modeling error and disturbances, but cannot be solved by the standard procedures. In order to solve the LMI problem and to identify the chaotic systems as T-S fuzzy modes, we propose a compound optimization strategy called the island-based random-walk algorithm (IRA). The algorithm is composed of a set of communicating random-walk optimization procedures concatenated with the down-hill simplex method. The design procedure and validity of the proposed scheme is demonstrated via numerical simulation of the dithered fuzzy control of a chaotic system.     

Static Bayesian games with finite fuzzy types and the existence of equilibrium

This paper studies a special game with incomplete information, in which the payoffs of the players are both random and fuzzy. Such a game is considered in the context of a Bayesian game with the uncertain types characterized as fuzzy variables. A static fuzzy Bayesian game is then introduced and the decision rules for players are given based on credibility theory. We further prove the existence of the equilibrium of the game. Finally, a Cournot competition model with fuzzy efficiency under asymmetric information is investigated as an application and some results are presented.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.