基于软计算协作技术的智能评审管理系统

基于模糊系统、神经网络、遗传算法和粗糙集等软计算的协作技术，建立了科研项目的立项评审智能管理系统。运用软件工程原理与方法，对该系统及其在科研项目立项评审的应用软件进行计划、开发和维护。实际应用表明了该系统的可行性和有效性，并可推广于其他智能管理系统。     

基于GA-BP的模糊神经网络控制器与Elman辨识器的系统设计

提出了一种基于神经网络的模糊控制系统 ,该系统由模糊神经网络控制器和模型辨识网络组成 .文中介绍了模糊神经网络控制器采用遗传算法离线优化与 BP算法在线调整 ,给出了具体控制算法 ,推导了变形 Elmam网络的系统辨识算法 .仿真结果表明了此法的可行性和有效性 .     

模糊神经网络在数据融合技术中的应用

本文主要阐述了模糊神经网络技术，尤其是模糊联结聚合神经网络技术在数据融合技术中的理论与应用。     

模糊RBF神经网络在音频零水印中的应用

针对当前零水印不能"嵌入"有意义水印的不足,构建了在小波域中基于神经网络的零水印系统,提出了一种基于模糊RBF神经网络的音频零水印方案,有效解决了音频水印的鲁棒性与透明性之间的矛盾.模糊神经网络模糊系统的隶属度函数和推理规则决定RBF神经网络的结构和学习算法.因为水印方案不改变原始音频数据,所以具有良好的透明性,实验结果表明,方案具有很强的鲁棒性.     

小卫星在轨故障诊断的模糊神经网络模型

小卫星高性能，高自主的发展趋势对于在轨故障诊断技术的实现要求日益迫切，而受小卫星体积小，重量轻，能源少的限制，当前常用的建立在高性能计算机硬件基础上的各种诊断方法不再适用于强调实时性，准确性的在轨运行监测，诊断与恢复和重构重处理。本文小卫星一体化系统总体设计技术研究与集成化设计系统为基础，采用一种神经网络与模糊系统相结合的模糊神经网络（ＦＮＮ）模型来分区域表示诊断系统并基于该ＦＮＮ模型进行诊断推量     

基于模糊神经网络建模的RFID室内定位算法

将模糊神经网络FNN应用于基于RFID技术的室内定位系统IPS,提出一种基于模糊神经网络的RFID室内定位算法,算法将参考标签数据作为神经网络的训练样本,建立"标签接收信号强度与标签读写器间距离RSSI-DIST"的映射模型,然后利用最小二乘解确定目标的位置坐标.同时,对比了传统BP神经网络和FNN网络在建模和定位中的性能.在仿真和硬件平台测试中,模糊神经网络都要比BP表现出更优异的性能,表明基于模糊神经网络的算法更适合于IPS系统.     

量子粒子群智能算法在国际布伦特原油价格预测中的应用

原油价格的波动对世界经济政治形势具有重要的作用,其预测问题是维护原油生产、消费企业及国家利益的重大问题。因此,原油价格预测是国际市场研究的一个重要领域。本文将TSK模糊逻辑系统与神经网络结合,设计五层模糊神经网络系统,采用量子粒子群(QPSO)智能算法调整模糊神经网络系统的参数,将所设计的智能系统应用于国际布伦特原油价格预测中。并将QPSO算法与BP算法和最小二乘法进行比较,预测性能指标和仿真结果表明基于QPSO智能算法的模糊神经网络系统的设计是有效的,取得了更好的效果。     

信息融合中提高信源置信度的方法研究

在分析信息融合和模糊神经网络理论的基础上,构造出具有质量信息的模糊神经网络信息融合结构.通过模糊神经网络对信源本身、环境因素、人为因素等各种因素的处理给出各个信源的置信度因子,再将置信因子与各信源的报告数据统一进行融合处理,可提高各信源的可信度,从而提高融合系统的可靠性和有效性,使系统的整体性能加强.     

基于集值统计的模糊神经网络专家系统及其应用

建立基于集值统计的模糊神经网络专家评审系统，并应用于科研项目评审工作，实际应用表明该系统是可行的。     

前向正则模糊神经网络依K-积分模的泛逼近能力

针对前向正则模糊神经网络引进K-拟可加积分和K-积分模概念,应用积分转换定理研究了该网络在K-积分模意义下对模糊值简单函数类的泛逼近能力,进而在有限K-拟可加测度空间上,借助模糊值简单函数为桥梁获得了前向正则模糊神经网络依K-积分模对(u)-可积有界模糊值函数类仍具有泛逼近性.该结果表明前向正则模糊神经网络对连续模糊系统的逼近能力可以推广为对一般可积系统的逼近能力.     

Ten years of genetic fuzzy systems: current framework and new trends

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms.The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems. After a brief introduction to models and applications of genetic fuzzy systems, the field is overviewed, new trends are identified, a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated, and open questions that remain to be addressed in the future are raised. The paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems.     

Intelligent systems in finance

Business sectors ranging from banking and insurance to retail, are benefiting from a whole new generation of ‘intelligent’ computing techniques. Successful applications include asset forecasting, credit evaluation, fraud detection, portfolio optimization, customer profiling, risk assessment, economic modelling, sales forecasting and retail outlet location. The techniques include expert systems, rule induction, fuzzy logic, neural networks and genetic algorithms, which in many cases are outperforming traditional statistical approaches. Their key features include the ability to recognize and classify patterns, learning from examples, generalization, logical reasoning from premises, adaptability and the ability to handle data which is incomplete, imprecise and noisy. This paper is the first in a series to appear in Applied Mathematical Finance;here we introduce the reader to the basic concepts of intelligent systems, describe their mode of operation and identify applications of the techniques in real world problem domains. Subsequent papers will concentrate on neural networks, genetic algorithms, fuzzy logic and hybrid systems, and will investigate their history and operation more rigorously.     

Fuzzy regression analysis using neural networks

In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can be applied to more complex systems than the existing LP based methods. First we propose learning algorithms of neural networks for determining a nonlinear interval model from the given input-output patterns. A nonlinear interval model whose outputs approximately include all the given patterns can be determined by two neural networks. Next we show two methods for deriving nonlinear fuzzy models from the interval model determined by the proposed algorithms. Nonlinear fuzzy models whose h-level sets approximately include all the given patterns can be derived. Last we show an application of the proposed methods to a real problem.     

Robust passivity analysis of fuzzy Cohen-Grossberg BAM neural networks with time-varying delays

This paper is concerned with the problem of passivity analysis for a class of Cohen-Grossberg fuzzy bidirectional associative memory (BAM) neural networks with time varying delay. By employing the delay fractioning technique and linear matrix inequality optimization approach, delay dependent passivity criteria are established that guarantees the passivity of fuzzy Cohen-Grossberg BAM neural networks with uncertainties. The passivity condition is expressed in terms of LMIs, which can be easily solved by various convex optimization algorithms. Finally, a numerical example is given to illustrate the effectiveness of the proposed result.     

A fast genetic method for inducting descriptive fuzzy models

Under certain inference mechanisms, fuzzy rule bases can be regarded as extended additive models. This relationship can be applied to extend some statistical techniques to learn fuzzy models from data. The interest in this parallelism is twofold: theoretical and practical. First, extended additive models can be estimated by means of the matching pursuit algorithm, which has been related to Support Vector Machines, Boosting and Radial Basis neural networks learning; this connection can be exploited to better understand the learning of fuzzy models. In particular, the technique we propose here can be regarded as the counterpart to boosting fuzzy classifiers in the field of fuzzy modeling. Second, since matching pursuit is very efficient in time, we can expect to obtain faster algorithms to learn fuzzy rules from data. We show that the combination of a genetic algorithm and the backfitting process learns faster than ad hoc methods in certain datasets.     

多层前向正则模糊神经网络的逼近能力

Abstract. Four-layer feedforward regular fuzzy neural networks are constructed. Universal ap-proximations to some continuous fuzzy functions defined on (R)“ by the four-layer fuzzyneural networks are shown. At first,multivariate Bernstein polynomials associated with fuzzyvalued functions are empolyed to approximate continuous fuzzy valued functions defined on eachcompact set of R“. Secondly,by introducing cut-preserving fuzzy mapping,the equivalent condi-tions for continuous fuzzy functions that can be arbitrarily closely approximated by regular fuzzyneural networks are shown. Finally a few of sufficient and necessary conditions for characteriz-ing approximation capabilities of regular fuzzy neural networks are obtained. And some concretefuzzy functions demonstrate our conclusions.     

基于知识的模糊神经网络的旋转机械故障诊断

提出了一种基于知识的模糊神经网络并用于故障诊断．首先基于粗糙集对样本数据进行初步规则获取,并计算规则的依赖度和条件覆盖度,然后根据规则数目进行模糊神经网络结构部分设计,规则的依赖度和条件覆盖度用于设定网络初始权重,而用遗产算法对神经网络输出参数进行优化．这样的模糊神经网络称为基于知识的模糊神经网络．使用该网络对旋转机械常见故障进行诊断,结果表明,和一般模糊神经网络相比,该网络具有训练时间短而诊断率高的特点．     

Dynamics of fuzzy impulsive bidirectional associative memory neural networks with time-varying delays

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy bidirectional associative memory (BAM) neural networks with impulsive effect and time-varying delays is investigated. The model of fuzzy impulsive BAM neural networks with time-varying delays established as a modified TS fuzzy model is new in which the consequent parts are composed of a set of impulsive BAM neural networks with time-varying delays. Further the exponential stability for fuzzy impulsive BAM neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique without tuning any parameters. In addition, an example is provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.