一种模糊神经网络自适应预测控制方案的研究

提出了一种模糊神经网络自适应预测控制方案,对学习公式进行了理论推导,并结合误差补偿以提高预测控制的精度。仿真实验表明,该算法可实现模糊控制和神经网络的优势互补,对非线性复杂系统具备良好的控制性能     

自适应模糊神经网络控制在电阻加热炉中的应用

提出一种自适应模糊神经网络控制器,着重讨论了自适应模糊神经网络的混合学习算法和自适应动量解耦的最速下降。人出了适于非线性时滞、基于径向基函数网络和自适模糊神经网络控制器的控制方案,并把它用在电阻加热炉中。实际应用表明,模糊神经网络控制器 具有良好的控制效果。     

模糊神经网络在时间序列预测中的应用

文中提出了将模糊聚类与梯度算法相结合的一种改进的训练模糊神经网络的混合型算法。模拟结果表明，模糊神经网络可以成功地用于时间序列的预测，模糊神经网络的训练速度与模拟精度都优于传统多层ＢＰ网络。     

ThinkDesign在模具板料成形回弹中的应用

一、克服零件回弹的技术要求 由于对汽车轻量化设计要求的不断提高,高强度钢板材料占汽车车身的比重越来越大.高强度钢可以在汽车轻量化的前提下,满足碰撞强度要求,然而用一般冲压方法生产出的高强度钢零件回弹值会非常大,回弹后的零件形状往往不在误差允许范围控制内,不能适应实际所需,这使车身零件回弹对车身品质的影响日益突出.     

模糊神经网络在电力短期负荷预测中的应用

提出用于电力短期负荷预测(SILF)的一种模糊神经网络(FNN)方法，该方法针对BP网络收敛速度慢、易导致局部极小值的缺点，将考虑气候、温度、星期类型等影响因素的模糊技术与快速二阶BP网络相结合，并以南方电网负荷预测为例，应用MATLAB蚀语言对系统进行仿真训练，测试结果表明，该方法具有较高的预测精度。     

自适应模糊神经网络研究

模糊神经网络提供了从人工神经网络中模糊规则的抽取。本文研究模糊神经网络的自适应学习，规则插入和抽取及神经－模糊推理的FuNN模型，把遗传算法作为系统模糊规则选择的自适应策略之一。     

预测板料形状和尺寸——板材冲压成形预测软件FASTBLANK

一个已掌握CAD、CAM技术的制造厂家,更关心的是冲压件能否成形,产品质量是否合格.冲压件几何形状的复杂性造成冲压成形过程中板材成形性难以估计,致使设计人员往往不能预知模具设计是否正确.     

基于快速算法的模糊神经网络自适应控制

针对过程控制中被控对象常具有非线性、不确定性及参数时变等复杂因素,而难以建立精确的数学模型的情况,提出了一种基于快速学习算法的模糊神经网络自适应预测控制方案。该方案用神经网络作辨识器,模糊神经网络作控制器来实现非线性系统的自适应预测控制。为了克服传统的梯度下降法收敛速度慢、容易陷入局部极小值的缺点,该方案采用递推最小二乘法训练模糊神经网络。仿真结果表明,该方案可以实现模糊控制和神经网络的优势互补,对不确定非线性系统具有很好的控制效果。     

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。     

自适应模糊系统理论在负荷预测中的应用研究

应用自适应模糊系统理论的最新成果实现实用化的预测系统,并以电力电负荷预测为具体应用背景完成了实验研究,系统的建立和运行分别依赖于对历史数据和对实时数据的离线和在线学习,具有明显的自适应性和鲁棒性,通过合理的设计实现简洁的系统结构,通过在线训练确定优化的系统设置,短期负荷预测的日均相对误差小于２％,可以满足现场的实用化要求。     

计算机仿真在冲压回弹中的应用研究

该文运用仿真计算的方法，采用板料成型数值模拟国际会议NUMISHEET的标准算例，系统地研究了各种西素对冲压件回弹的影响，比较了它们对回弹抑制的效果，并讨论了回弹的控制方法．这对于生产中制定合理的冲压工艺方案，控制和减步冲压件的回弹．提高产品的尺寸精度有重要指导意义和实用价值。     

Prediction of ground motion parameters using randomized ANFIS (RANFIS)

In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The three time domain ground motion parameters which are predicted by the model are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using the database released by PEER (Pacific Earthquake Engineering Research Center). Each ground motion parameter is related to mainly to four seismic parameters, namely earthquake magnitude, faulting mechanism, source to site distance and average soil shear wave velocity. The experimental results validate the improved performance of the machine, with lesser computation time compared to prior studies.     

基于进化策略的板材回弹优化建模研究

以回弹最小为目标提出了一种有效的成形工艺优化方法.通过有限元方法对回弹过程进行建模和分析,以获得不同成形工艺条件下的回弹量作为神经网络的样本信号.利用RBFN来模拟复杂的回弹过程.采用改进的进化策略(ES)算法对已建立的回弹模型进行优化以获得最小回弹.结果表明,提出的RBF网络与ES相结合的方法具有全局搜索特性,对于存在不可微的目标函数的非线性优化问题,能以较快的速度和较大概率收敛于全局最优解.     

自适应神经模糊系统在托砖架最高温度预测中的应用

气流床气化炉在气化工业中得到了广泛应用，托砖架用于支撑气化炉耐火砖的重量，必须有足够的强度，温度是托砖架强度的重要影响因素。传统方法使用有限单元法计算，需要编制复杂的计算机程序。本文使用已有软件计算出托砖架最高温度点温度，对一个自适应神经模糊系统(ANFIS)进行训练，然后对未知数据进行预测，取得了准确的结果。     

基于ANFIS混合模型的短时交通流预测

城市短时交通流预测可以帮助人们选择出行最优路线,提高出行效率,其研究在交通拥堵日益严重的今天十分必要.受天气等多种因素影响,短时交通流的精确预测比较困难,为改善短时交通流预测的精度,本文提出了一种基于自适应模糊推理系统（ANFIS）的混合模型.该混合模型用周期性知识模型及残差数据驱动ANFIS模型集成得到.为验证所提出的混合模型的性能,与倒向传播神经网络（BPNN）模型和普通ANFIS模型进行对比.实验结果证明混合模型在交通流预测方面有更好的适用性和准确度.     

改进的ANFIS在房产评估中的应用

房产评估是个较为复杂的非线性过程,目前的方法存在房产近邻难以定义等问题。为解决这一问题,提出基于k近邻的自适应神经模糊推理方法,并应用于房产评估。该方法通过定义不同意义的全变量、部分变量、空间、时空的k近邻,计算k近邻均价,将k近邻均价加入模型。实验结果表明,使用基于空间k近邻和时空k近邻改进自适应神经模糊推理方法对房产价格进行预测,准确性显著提高。     

Application of firefly algorithm and ANFIS for optimisation of functionally graded beams

Volume fraction optimisation of functionally graded beams is studied for maximising the fundamental natural frequency by applying a new meta-heuristic nature-inspired algorithm called firefly algorithm (FA) which is based on the flashing behaviour of fireflies. Nature-inspired algorithms are among the most powerful algorithms for optimisation of engineering problems. The primary optimisation variables are the three parameters in the power-law distribution. Since the search space is large, the optimisation processes becomes so complicated and too much time consuming. Thus, a suitable Adaptive Neuro-Fuzzy Inference System (ANFIS) that is based on Takagi–Sugeno fuzzy inference system is combined with FA to reproduce the behaviour of the structure in free vibration. The ANFIS improves the speed of optimisation process by a considerable amount. The results are compared with those obtained by imperialist competitive algorithm, genetic algorithm and Artificial Neural Networks proposed in our previous work. Results show that the combination of FA and ANFIS is capable of yielding better optimal solution in comparison with other available techniques. It is believed that new results are of interest to the scientific and engineering community in the area of engineering design.     

基于自适应神经模糊法的PEMFC温度建模

质子膜燃料电池（PEMFC）工作被认为是21世纪最有希望的绿色发电技术,其原理涉及热力学、电化学、流体力学、传质学等理论,形成一个非线性复杂系统,难以建立数学模型;因此,利用模糊逻辑系统和人工神经网络具有为非线性系统建模的较强的逼近能力以及自学习能力,采用自适应神经模糊算法,建立PEMFC温度特性模型;利用测试数据作为训练样本,在氧气压力给定的条件下,以空气（或氧气）压力和冷却水作温度为模型的输入量,电池的工作温度为输出量,建立了3种不同PEMFC温度特性模型;表明该方法具有简单、可行、精度高等优点。并为PEMFC控制系统的设计和电池性能的优化提供了基本依据。     

Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS

The management of concrete quality is an important task of concrete industry. This paper researched on the structured and unstructured factors which affect the concrete quality. Compressive strength of concrete is one of the most essential qualities of concrete, conventional regression models to predict the concrete strength could not achieve an expected result due to the unstructured factors. For this reason, two hybrid models were proposed in this paper, one was the genetic based algorithm the other was the adaptive network-based fuzzy inference system (ANFIS). For the genetic based algorithm, genetic algorithm (GA) was applied to optimize the weights and thresholds of back-propagation artificial neural network (BP-ANN). For the ANFIS model, two building methods were explored. By adopting these predicting methods, considerable cost and time-consuming laboratory tests could be saved. The result showed that both of these two hybrid models have good performance in desirable accuracy and applicability in practical production, endowing them high potential to substitute the conventional regression models in real engineering practice.     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.