基于GA的ANFIS在函数拟合中的应用

本文介绍了一种遗传算法(CA)优化自适应神经模糊推理系统(ANFIS)的方法,并采用基于GA优化ANFIS方法,拟合非线性多峰函数,同时分析了这种方法的拟合能力和预测能力.实验结果表明,加入GA优化后的ANFIS具有更加优秀的拟合能力和预测能力,更适合于用来建立复杂参数问的非线性映射关系.     

基于ANFIS的非线性电机系统的建模

将一种种神经－模糊结构－自适应神经模糊推理系统（简称ANFIS）用于非线性电机系统的建模，获得了一个良好的大范围的全局非线性模型，同时，通过与反向传播网络建模结果的性能对比，说明ANFIS在参数收敛速度及建模精度上的优越性，显示出ANFIS是非线性系统的建模，辨识的有力工具。     

基于自适应神经模糊推理系统的非线性系统控制

由于非线性系统具有模糊性、不确定性、非线性等特点，所以常常使用模糊控制来对其实现控制，但常规的模糊控制系统存在着一定的问题。该文把神经网络与模糊控制相结合，介绍了自适应神经元模糊推理系统ANFIS(Adaptive Neuro—Fuzzy Inference System)的基本结构，并将ANFIS用于典型的非线性系统控制中，仿真结果表明训练后的ANFIS能很好地控制实际的对象。     

基于ANFIS的非线性系统辨识研究

系统辨识是控制系统设计的基础，对非线性系统进行辨识是当前的难点；文献[1]提出了用模糊建模方法，文献[2]提出了用神经网络方法，在总结上述方法不足的基础上，该文提出了用自适应神经模糊推理系统(ANFIS)对非线性系统进行辨识的方法，仿真结果表明，ANFIS进行非线性系统辨识是可行的，其辨识精度很高。     

ANFIS在非线性系统建模与消噪中的应用

给出了自适应神经模糊推理系统(ANFIS)的一般描述，并应用ANFIS进行非线性系统建模和消除信号中的噪声，以此改进工业控制系统中非线性系统的控制性能。仿真表明ANFIS具有较高的收敛速度和建模精度，是非线性系统建模的有力工具。     

基于ANFIS的非线性电机系统的建模

将一种神经—模糊结构—自适应神经模糊推理系统 (简称ANFIS)用于非线性电机系统的建模 ,获得了一个良好的大范围的全局非线性模型 ,同时 ,通过与反向传播网络建模结果的性能对比 ,说明ANFIS在参数收敛速度及建模精度上的优越性。显示出ANFIS是非线性系统的建模、辨识的有力工具     

基于ANFIS的机器人系统建模的研究

针对机器人这种不确定性的复杂非线性系统很难建立其精确的数学模型这一问题,提出一种基于自适应神经模糊推理(ANFIS)的方法对机器人系统进行建模.此方法将模糊推理和神经网络的学习能力有机地结合起来,并利用神经网络的学习机制自动地从输入输出数据中提取规则.建模过程中为了给ANFIS赋予一个合适的初始状态,选用减法聚类对输入数据进行处理.ANFIS网络的所有参数采用混合算法进行调节,即前提参数采用误差反向传播法,结论参数采用最小二乘法.最后在Matlab中对二自由度机器人进行仿真研究,仿真结果表明该方法模型结构简单,建模速度快,辨识精度高,同时也验证了该方法的有效性,为进一步实现机器人鲁棒自适应控制打下基础.     

基于神经模糊系统的热工过程建模及预测

热工对象内部过程的物理性能比较复杂,其往往表现出非线性、严重时变、大迟延和不确定等特点,这就使得难以对其建立比较精确的模型。该文以自适应神经模糊推理系统(ANFIS)作为辨识器建立热工过程模型,用ANFIS分别建立锅炉-汽轮机的非线性模型、不同负荷工况点的线性模型,并根据现场采集的锅炉-汽轮机系统数据建立了ANFIS模型。对以上三个系统的建模仿真结果表明基于ANFIS建立的模型具有较高的模型精度和较好的预测能力,ANFIS可用于非线性系统、复杂系统的建模和预测,并具有较少的训练次数和较小的预测误差。     

基于ANFIS的糖厂PH中和过程建模

针对糖厂pH中和过程具有强非线性,大滞后性,不确定性等特点,将模糊推理系统和神经网络相结合,介绍了一种自适应神经模糊推理系统（ANFIS）,并建立了pH中和过程的模型。仿真结果表明,利用ANFIS所建立的模型能很好地逼近实际的非线性系统,并且辨识精度高,泛化能力强,为后续的优化控制研究奠定了基础。     

基于ANFIS的有色噪声抵消技术

利用自适应神经模糊推理系统ANFIS对噪声的非线性动态特性进行建模,并利用ANFIS逼近有色噪声,然后从测量信号中消除有色噪声得到有用的信号。仿真结果表明利用这种方法能在和被测对象相似的噪声背景中很好地提取有用信号。     

Integration of feedforward neural network and finite element in the draw-bend springback prediction

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elastically-driven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data.     

The effect of non-linear recovery on springback prediction

Numerical prediction of springback remains one of the major problems in sheet metal forming. The quality of springback prediction for a sheet metal forming process depends on a precise estimation of the stress distribution all over the metal sheet. In particular, the decrease of the unloading elastic modulus (non-linear recovery) with increasing plastic prestrain during stress reversals, observed by several authors, was commonly not taken into account in FE analysis. A model is proposed which takes the elastic modulus evolution with plastic prestrain and the unloading stress into account. Numerical simulations show that this model coupled with a non-linear kinematic hardening model can accurately predict the springback amount.     

Optimization methods for complex sheet metal stamping computer aided engineering

Nowadays, sheet metal stamping processes design is not a trivial task due to the complex issues to be taken into account (complex shapes forming, conflicting design goals and so on). Therefore, proper design methodologies to reduce times and costs have to be developed mostly based on computer aided procedures. In this paper, a computer aided approach is proposed with the aim to offer a methodology able to solve very complex sheet metal stamping processes, in particular a progressive design approach based on the integration between numerical simulations and optimization methodologies is presented. In particular, Response Surface Method, Moving Least Squares approximation and Pareto optimal solutions search techniques were applied in order to design two different complex 3D stamping operations. The proposed design procedure is able to verify the necessity of a spatially differentiated restraining forces approach and to design the best policy for them. In particular, different part “quality” indicators were monitored such as springback occurrence and thinning. An explicit/forming-implicit/springback approach was utilized to develop the numerical simulations. To sum up, a new and flexible design methodology is proposed, able to: deal with complex sheet metal stamping processes; investigate many possible technological scenarios; carry out a set of reliable solutions able to satisfy different design requirements; offer different optimization possibilities in order to take in to account all the sheet metal stamping design issues.     

Optimization of variable blank holder force trajectory for springback reduction via sequential approximate optimization with radial basis function network

Springback is one of the major defects in sheet metal forming. Variable blank holder force (VBHF) approach is one of the effective ways for the springback reduction. In this paper, the VBHF trajectory is optimized to reduce the springback by a sequential approximate optimization (SAO) with radial basis function (RBF) network. The U-shaped forming in NUMISHEET’93 is employed to determine an optimum VBHF trajectory, for example. In this paper, the bending moment is taken as the objective function. The tearing of sheet during the forming is considered as the design constraint, and the forming limit diagram (FLD) is employed to evaluate the design constraint quantitatively. It has been found from numerical results that the optimal VBHF trajectory can drastically reduce the springback in comparison with various VBHF trajectories. Through the theoretical examination and numerical simulation, the springback reduction of metal forming by the VBHF trajectory is discussed.     

In-process angle measurement and control for flexible sheet metal manufacture

A new method of controlling springback in small-radius pressbrake bending operations has been developed. This method provides a more accurate bending process, necessary for the further development of precision, small-lot sheet metal assembly manufacture. The pursuit of this research has led to the development of an inexpensive, high-resolution, on-line angle sensor. In addition, a simplified analytic model of the bending process was developed to predict springback in terms of material and tooling geometry variables. Finally, a springback control system has been developed with demonstrated accuracy of one-third of a degree in right-angle bends for cold-rolled steel samples covering a range of material properties.     

Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites

This article introduces an adaptive network-based fuzzy inference system (ANFIS) model and two linear and nonlinear regression models to predict the compressive strength of geopolymer composites. Geopolymers are highly complex materials which involve many variables which make modeling its properties very difficult. There is no systematic approach in the mix design for geopolymers. The amounts of silica modulus, Na₂O content, w/b ratios, and curing time have a great influence on the compressive strength. In this study, by developing and comparing parametric linear and nonlinear regressions and ANFIS models, we dealt with predicting the compressive strength of geopolymer composites for possible use in mix-design framework considering the mentioned complexities. ANFIS model developed by generalized bell-shaped membership function was recognized the best approach, and the prediction results of linear and nonlinear regression models as empirical methods showed the weakness of these models comparing ANFIS model.

     

基于JSTAMP/NV的汽车横梁回弹分析

为测试JSTAMP／NV的回弹仿真精度,以2005年国际板材成型数值模拟会议（Numisheet’2005）提出的预测汽车横梁回弹的基准考题为例,采用Y—u材料模型,基于JSTAMP／NV仿真冲压拉深成型和回弹的过程．仿真结果与试验结果吻合良好,表明利用JSTAMP／NV能有效预测汽车横梁的回弹．     

Comparison of regression and artificial neural network model for the prediction of springback during air bending process of interstitial free steel sheet

This paper compares the regression and neural network modeling for predicting springback of interstial free steel sheet during air bending process. In this investigation, punch travel, strain hardening exponent, punch radius, punch velocity and width of the sheet were considered as input variables and springback as response variable. It has been observed that the ANN modeling process has been able to predict the springback with higher accuracy when compared with regression model.     

Optimal process design of sheet metal forming for minimum springback via an integrated neural network evolutionary algorithm

The process of sheet metal forming is characterized by various process parameters. Accurate prediction of springback is essential for the design of tools used in sheet metal forming operations. In this paper, an evolutionary algorithm is presented that is capable of handling single/multiobjective, unconstrained and constrained formulations of optimal process design problems. To illustrate the use of the algorithm, a relatively simple springback minimization problem (hemispherical cup-drawing) is solved in this paper, and complete formulations of the algorithm are provided to deal with the constraints and multiple objectives. The algorithm is capable of generating multiple optimal solutions in a single run. The evolutionary algorithm is combined with the finite element method for springback computation, in order to arrive at the set of optimal process parameters. To reduce the computational time required by the evolutionary algorithm due to actual springback computations via the finite element method, a neural network model is developed and integrated within the evolutionary algorithm as an approximator. The results clearly show the viability of the use of the evolutionary algorithm and the use of approximators to derive optimal process parameters for metal forming operations.