液压系统故障诊断的高阶统计量-模糊神经网络法

利用高阶统计量模糊神经网络方法对液压系统故障进行诊断 ,解决低信噪比故障特征信号下的故障诊断问题。介绍了高阶统计量和模糊神经网络的基本原理 ,阐述了利用高阶统计量模糊神经网络诊断液压系统故障的方法 ,给出了以阀控液压缸系统为研究对象的诊断实例。试验结果表明 ,利用该方法对液压系统进行故障诊断可以有效地提高故障特征信号的信噪比 。

基于频域分解的模糊神经网络负荷预测方法研究

通过分析电力系统负荷特性,可保证电力系统持续可靠的供电及为电力用户提供良好的电能质量。分析了春秋季、夏季、冬季、气象平稳月等多时间维度,并对南方电网负荷变化进行了系统研究,采用气象灵敏度分析气象与负荷之间的关系。为进一步分析研究负荷内在关系,采用频域分解技术对负荷进行解析研究。综合考虑以上因素提出考虑气象的模糊神经网络预测方法,确定影响负荷特性的主要因素并挖掘隐藏在负荷特性指标间的内在规律。

汽轮机故障诊断的模糊神经网络方法

为有效进行汽轮机故障诊断 ,本文提出了将模糊理论与自适应变结构神经网络相结合的方法 ,并对其工作原理进行了阐述。最后通过实例 ,说明该方法是可行的。

河道砂体含油性预测方法研究

利用Kohonen网络、模糊神经网络和支持向量机等方法,对胜利油田埕东凸起北坡河道砂体的含油性进 行了预测。对其中1号河道砂体预测结果的分析表明,3种方法各具特点,Kohonen网络法因为使用了多属性聚 类的结果,因此与研究目标的关系比较直观;模糊神经网络法充分考虑了河道砂体内部存在的差异性;支持向量 机法完整地利用了地震波的属性。与井点的含油性对比,3种方法的预测效果中支持向量机法最好,Kohonen 网络法次之,模糊神经网络法稍差。综合应用各种预测方法,可以使预测结果更加准确。

城市应急物流中心多目标选址模型及方法研究

针对城市应急物流中心选址问题,建立了考虑覆盖率、总时间成本的多目标0~1整数模型和评价模型,并利用深度优先搜索法和模糊神经网络法来求解该模型。为验证所提出的模型及方法,以南京市江宁区为例,设计了算例实验,同时将多目标模型和单目标模型进行对比,分析多目标模型对选址方案的影响,并对选址方案及相关影响因素进行评价,最终实验结果验证了模型和方法的有效性,可为城市突发事件应急设施选址提供参考依据。

包装产品环境性能的评价模式及方法（一）

对国内外包装产品环境性能评价的四种模式及方法：生命周期评价LCA法，加权简化定性LCA法，模糊层次分析的绿色度评价法和采甩模糊神经网络的综合评价法进行了比较研究，分析了各自的原理、特点及适应性，指出了比较的结论及建议。

既有居住建筑节能改造全寿命周期费用估算分析

对节能改造全寿命周期费用进行估算,对于节能改造投资决策与实施都有着显著的现实意义,将节能改造全寿命周期划分为前期准备期、改造实施期与维保运营期,识别了节能改造全寿命周期费用,将模糊神经网络应用于节能改造费用估算过程中,构建了改造实施阶段费用模糊神经网络估算模型,并通过实际案例验证了该模型的准确性与适用性。研究表明利用模糊神经网络模型可对节能改造项目费用实施估算,结果可靠,具有定的适用性。后提出了若干实用性建议。

最优加权几何平均组合预测在短期电价预测中的应用

准确的短期电价预测可为市场参与者的竞价策略提供指导,直接影响着参与者的利益。针对电价预测的精确度问题,引入了最优加权几何平均组合预测方法,它综合利用了二次指数平滑、自适应模糊神经网络和修正的灰色模型三种方法提供的有用信息,并且该组合预测模型的误差平方和小于各单一预测方法的误差平方和,因此进一步提高了预测结果的准确性。最后用算例验证了该组合预测方法的可行性。

长三角区域科学中心城市创新要素流动力评价

创新要素的流动是加强城市群一体化建设,实现创新载体间交流互动的重要途径,对提升创新绩效和加强长三角区域创新体系的建设具有重要意义。文章通过定义和筛选长三角区域科学中心城市,从主体、人才、投入、产出以及环境五个维度构建创新要素流动力评价指标体系,结合T-S模糊神经网络算法对长三角区域科学中心城市的创新要素流动力进行评价并预测其发展趋势。结果表明:从人才维度来看,高校人才的大幅流动对要素流动力的提升起支撑作用;从投入维度来看,R&D经费外部支出起绝对支撑作用;从产出维度来看,合作论文量影响最大;从环境维度来看,区域经济发展水平起到决定作用;从整体流动力体系来看,流动环境重要程度平均占比达67%,流动人才与主体发展动力不足。

A REALIZATION OF FUZZY LOGIC BY A NEURAL NETWORK

This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.

包装产品环境性能评价模式的比较研究

对国内外包装产品环境性能评价的4种模式及方法:生命周期评价LCA法、加权简化定性LCA法、模糊层次分析的绿色度评价法和采用模糊神经网络的综合评价法,进行了比较研究,分析了各自的特点及评价性能,提出了比较的结论及建议。

New Structural Self-Organizing Fuzzy CMAC with Basis Functions

To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing.

Research on Prediction of Red Tide Based on Fuzzy Neural Network

In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.

Research on fuzzy neural network algorithms for nonlinear network traffic predicting

This paper addresses the use of fuzzy neural networks （FNN） for predicting the nonlinear network traffic. Through training the fuzzy neural networks with momentum back-propagation algorithm （MOBP） and choosing the appropriate activation function of output node, the traffic series can be well predicted by these structures. From the effective forecasting results obtained, it can be concluded that fuzzy neural networks can be well applicable for the traffic series prediction. In addition,the performance of the FNN was particularly discussed and analyzed in terms of prediction ability compared with solely neural networks. The effectiveness of the oroBosecl FNN is demonstrated through the simulation.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.

Intelligent anti-swing control for bridge crane

A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.

Simultaneous Perturbation Stochastic Approximation Algorithm Combined with Neural Network and Fuzzy Simulation

In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

A research on an energy-saving software for pumping units based on FNN intelligent control

An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The structure of the Takagi-Sugeno (T-S) fuzzy neural network model is introduced and modified. FNNs are trained with sample information from oil fields and expert knowledge. Finally, pumping unit energy-saving FNN software, which cuts down power costs substantially, is presented.

A NEURAL FUZZY INFERENCE SYSTEM

This paper proposes a new neural fuzzy inference system that mainly consists of four parts. The first part is about how to use neural network to express the relation within a fuzzy rule. The second part is the simplification of the first part, and experiments show that these simplifications work. On the contrary to the second part, the third part is the enhancement of the first part and it can be used when the first part cannot work very well in the fuzzy inference algorithm, which would be introduced in the fourth part. Finally, the fourth part "neural fuzzy inference algorithm" is been introduced. It can inference the new membership function of the output based on previous fuzzy rules. The accuracy of the fuzzy inference algorithm is dependent on neural network generalization ability. Even if the generalization ability of the neural network we used is good, we still get inaccurate results since the new coming rule may not be related to any of the previous rules. Experiments show this algorithm is successful in situations which satisfy these conditions.