基于粗-模糊神经网络的决策控制

通过将模糊集和粗集,神经网络结合,提出了一种基于模糊规则的新的粗模糊神经网络,它通过利用误差反向传播算法实时修正该新型网络中的权值参数,从而能被有效地应用于不确定系统的决策分类与模式识别问题.最后通过对一个不确定决策系统的模式识别的仿真结果表明该粗模糊神经网络能大大提高模式识别决策的准确率.     

生态遗传算法在煤矸石模糊模式识别中的应用

给出煤矸石组分模式识别的模糊神经网络模型 ,提出一种实用生态算子 ,同时将在此基础上构建的生态遗传算法用于模糊神经网络的离线学习。仿真和实验结果显示 :新算法使网络具有良好的收敛性能 ,而且从训练好的定量网络中提取的模糊规则提高了煤中矸石的识别率。     

基于BP神经网络的模糊参数辨识

提出一种与TSK模糊模型相似的模糊模型—M-2模型,证明了M-2模型与一个4层前向神经网络是等价的,在此基础上提出基于BP神经网络的模糊模型参数辨别算法,即通过BP神经网络对样本数据的学习,直接从样本数据获取模型参数,建立M-2模糊模型,通过仿真实例验证了该算法的有效性。     

一种混合学习算法

提出了一种基于模糊逻辑和神经网络的自学习网络模型和一种结合自组织学习和BP学习的BPSOM混合学习算法。该模型通过BPSOM算法训练样本，能自动生成模糊逻辑规则，调节输入、输出变量的隶属函数；而且该算法比通常的BP学习算法收敛性好、速度快.     

基于模糊模式的手写数字识别

手写体数字识别是一个难度很大,但却具有广阔应用前景的研究课题.文章提出了一种基于模糊模式识别和BP神经网络技术对手写数字进行识别的算法:首先应用BP神经网络技术对手写数字样本进行学习,然后结合模糊模式识别方法进行手写数字识别.实验表明,该方法的正确识别率达95%以上.     

模糊神经网络在条形码识别中的应用

本文把模糊集合论的知识应用于自组织神经网络,提出一个模糊神经网络算法。新算法克服了一般神经网络方法共同面临的学习时间长、对网络参数敏感的弱点,广泛适用于一般的模式识别问题。对条形码识别问题的应用情况表明,新算法无论在网络学习的速度上还是在识别结果上都优于Ｋｏｈｏｎｅｎ的自组织神经网络方法。     

多模式对模糊联想记忆学习算法的优化

一、引言 人脑神经系统信息活动的一个重要特征是能够接收和处理模糊的、连续随机的信息,并在输出时不追求绝对精确解而只要求能找到问题的满意解。模糊联想记忆神经网络是Bart Kosko于1987年提出的采用模糊赫布型学习规则的一种单状态异联想记忆神经网络,在模糊控制、模式识别、专家系统等领域曾引起人们的关注。由于该网络的子集联想特性和不能有效地联想存储多个训练模式对而影响了它的应用[1-3]。本文提出模糊合成运算的一种微分法则,用一代价函数以反映网络性能,将梯度下降搜索技术与模糊赫布型学习规则相结合,建立了在单个模糊联想记忆神经网络中联想存储多个模式对的一种优化学习算法。理论分析和实例计算均证明该算法优于Kosko的学习规则。     

一种混合学习算法的研究

提出一种基于模糊逻辑和神经网络的自学习网络模型。该模型通过特定的学习算法训练样本，能自动生成模糊逻辑规则，调节输入、输出变量的隶属函数。同时，提出了一种结合自组织学习和ＢＰ学习的混合学习算法──ＢＰＳＯＭ，这种算法比通常的ＢＰ机学习算法收敛性好，速度快。     

一种混合学习算法的研究与应用

论文提出一种基于模糊逻辑和神经网络的自学习网络模型和一种结合自组织学习和BP学习的BPSOM混合学习算法。该模型通过BPSOM算法训练样本,能自动生成模糊逻辑规则,调节输入、输出变量的隶属函数;而且该算法比通常的BP学习算法收敛性好,速度快。仿真结果表明,利用该学习网络模型构造的同步发电机励磁控制器,能很好地稳定机端电压。     

Add-Mult型模糊神经网络

提出了一种Add-Mult型模糊神经网络模型(AMFNN)，给出了该模型的结构。根据梯度下降算法，给出了AMFNN模糊神经网络的误差反传学习算法。与6种极具代表性的模糊推理方法进行比较的结果表明，AMFNN模糊神经网络模型具有推理精度高、适用范围广、泛化能力强以及实现容易等特点，因而具有广阔的应用前景。     

人工神经网络设计算法的研究

人工神经网络在计算机领域中仍然是一个有待进一步研究的问题。文中介绍了目前使用的几种不同的人工神经网络设计算法：BP算法、FP算法、多层前向网络的交叉覆盖设计算法。探讨了这些算法的应用和特点，并从原理和设计思路上进行了分析。对BP算法和交叉覆盖算法给出具体的测试结果，解释并说明了各种算法的优缺点。重点研究并介绍了目前流行的多层前向网络的交叉覆盖算法的设计思路和应用方法，测试结果证明了该方法在智能识别领域的有效性。     

Black-, gray-, and white-box modeling of biogas production rate from a real-scale anaerobic sludge digestion system in a biological and advanced biological treatment plant

Neural Computing and Applications - Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling...     

A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks

Multilayered feedforward artificial neural networks (ANNs) are black boxes. Several methods have been published to extract a fuzzy system from a network, where the input–output mapping of the fuzzy system is equivalent to the mapping of the ANN. These methods are generalized by means of a new fuzzy aggregation operator. It is defined by using the activation function of a network. This fact lets to choose among several standard aggregation operators. A method to extract fuzzy rules from ANNs is presented by using this new operator. The insertion of fuzzy knowledge with linguistic hedges into an ANN is also defined thanks to this operator.     

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

基于规则的核电站故障诊断神经网络系统研制

人工神经网络方法越来越多地用于核电站诊断系统中,但是由于神经网络训练需要大量的训练样本并且诊断过程的不透明性,使得人工神经网络的应用受到限制。论文提出了一种人工神经网络故障诊断系统,结合了人工神经网络、模糊控制技术和专家系统的优点,使诊断过程易于理解,而且能够获得相应的解释,有更大的适应性。     

Determination of optimal hierarchical fuzzy controller parameters according to loading condition with ANN

This paper represents an artificial neural network (ANN) backpropagation algorithm is used to choose best coefficients of hierarchical fuzzy power system stabilizer (HFPSS). PSS is used for stability enhancement of a single machine infinite bus (SMIB) power system. ANN algorithm is used to predict load condition of the power system. And according to the predicted load condition ANN determinates choosing optimal parameters of the hierarchical fuzzy controller (HFC) to achieve better performance. Simulation results are compared with conventional PSS (CPSS) to show the effectiveness of the proposed controller. Also quantitative criterias of measuring performance is computed for 16 loading conditions.     

神经网络和模糊系统在故障诊断中的应用

本文提出了一种神经网络和模糊系统相结合的分级式故障诊断方法。神经网络通过对部分测量数据的处理,实现系统的回路级故障诊断,输出各回路故障出现的可信度。模糊系统通过对神经网络得到的初步诊断结果和其他测量值的处理,实现系统的元件级故障诊断,并对最终诊断结果作出解释。该方法融合了神经网络自适应学习能力强和模糊系统知识表达明确的优点,简化了神经网络学习数据获取及模糊推理规则建立的过程。通过对热硝酸冷却系统故障诊断的仿真,证明了该故障诊断方法的有效性。     

软件需求权重的模糊网络分析法

针对软件开发中顾客需求元素间的复杂关系,分析了层次分析法（AHP）与模糊层次分析法（FAHP）在解决复杂需求权重时所存在的缺陷;运用网络分析法（ANP）与梯形模糊数相结合的模糊网络分析法,建立加权极限超矩阵求解需求元素的混合权重;对比分析了模糊网络分析法、模糊层次分析法和网络分析法的权重结果;揭示了在复杂系统中元素的相互联系和顾客需求相对重要度评价的模糊性在权重的求解中的重要性;验证了模糊网络分析法在复杂需求权重求解中的可行性。研究结果为软件开发中顾客需求复杂权重的准确求解提供了依据。     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

基于神经网络的某超市水产品运营量预测模型研究

随着人工神经网络技术的不断成熟,人工神经网络和预测预报紧密结合起来。结合＂神经网络＋预测＂的开发模式,引入BP人工神经网络学习算法,构建了某超市水产品运营预测模型,并讨论不同结构的BP网络及随机初始化对预测结果的影响,同时进行了神经网络预测方法和其它预测方法的比较。