An adaptive neuro-fuzzy inference system for bridge risk assessment

Bridge risks are often evaluated periodically so that the bridges with high risks can be maintained timely. This paper develops an adaptive neuro-fuzzy system (ANFIS) using 506 bridge maintenance projects for bridge risk assessment, which can help Highways Agency to determine the maintenance priority ranking of bridge structures more systematically, more efficiently and more economically in comparison with the existing bridge risk assessment methodologies which require a large number of subjective judgments from bridge experts to build the complicated nonlinear relationships between bridge risk score and risk ratings. The ANFIS proves to be very effective in modelling bridge risks and performs better than artificial neural networks (ANN) and multiple regression analysis (MRA).     

Evolutionary neuro-fuzzy system for surface roughness evaluation

The paper presents a system that, according to the requirements referring to the product quality given in surface roughness, with minimum machining time and maximum metal removal rate, recommends optimal cutting parameters with the possibility of surface roughness control during the machining process. The suggested evolutionary neuro-fuzzy system for evaluation of surface roughness is composed of three units: surface roughness prediction by cutting parameters, multi-objective optimization of cutting parameters aimed at minimum machining time and maximum metal removal rate and control of obtained or required surface roughness by means of the features quantified from digital image of the observed machined surface. The paper outlines the idea and architecture of the system as well as the possibilities of implementation. The obtained results, illustrated by experimental research, justify the application and further development of the suggested evolutionary neuro-fuzzy system for evaluation of surface roughness within the given constraints.     

Self-spawning neuro-fuzzy system for rule extraction

In this paper we propose self-spawning neuro-fuzzy system (SSNFS), a new neuro-fuzzy system to derive fuzzy rules from data. The SSNFS model is based on a generic definition of incremental perceptron and a new learning algorithm that is capable of both structural (rule) learning and parametric learning. It constructs the fuzzy system by detecting a suitable number of rule patches and their positions and shapes in the input space. Initially the rule base consists of one single fuzzy rule; during the iterative learning process the rule base expands according to a supervised spawning validity measure. The rule induction process terminates when a given stop criterion is satisfied. SSNFS is very general since it does not require the prior knowledge about the input space and can be used in any application based on the scatter-partitioning fuzzy system. To demonstrate the effectiveness and applicability of our algorithm, we present a synthetic example and real-world modelling problems.

Computation of resonant frequency for equilateral triangular microstrip antennas using the adaptive neuro-fuzzy inference system

A new method based on the adaptive neuro-fuzzy inference system (ANFIS) for calculating the resonant frequency of the equilateral triangular microstrip patch antenna is presented. The ANFIS has the advantages of the expert knowledge of the fuzzy inference system and the learning capability of neural networks. A hybrid-learning algorithm, which combines the least-square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The results of the new method show better agreement with the experimental results, as compared to the results of previous methods available in the literature. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14, 134–143, 2004.     

An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

An expert system for used cars price forecasting using adaptive neuro-fuzzy inference system (ANFIS) is presented in this paper. The proposed system consists of three parts: data acquisition system, price forecasting algorithm and performance analysis. The effective factors in the present system for price forecasting are simply assumed as the mark of the car, manufacturing year and engine style. Further, the equipment of the car is considered to raise the performance of price forecasting. In price forecasting, to verify the effect of the proposed ANFIS, a conventional artificial neural network (ANN) with back-propagation (BP) network is compared with proposed ANFIS for price forecast because of its adaptive learning capability. The ANFIS includes both fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental result pointed out that the proposed expert system using ANFIS has more possibilities in used car price forecasting.     

基于自适应神经模糊系统的足球机器人射门点的确定

针对足球机器人射门行为中运算的高复杂性和反应延迟的局限,引入一种基于类高斯函数的自适应神经模糊推理系统（ANFIS）,用于确定最合适的射门点.系统由前件网络和后件网络构成,结合模糊逻辑理论,建立基于人类语言描述的射门行为模型.采用实际的比赛记录作为训练数据,离线地拟合系统输入与输出之间的映射关系,经训练的系统能够自动地调整前期隶属度函数的形状和后期的自适应权值.仿真结果表明,射门成功率和反应速度都能够达到预期的效果,方法的有效性得到了验证.     

An efficient compensatory neuro-fuzzy system and its applications

In this study, a compensatory neuro-fuzzy system (CNFS) is proposed. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of a neuro-fuzzy system to make the fuzzy logic system more adaptive and effective. Furthermore, an online learning algorithm that consists of structure learning and parameter learning is proposed to automatically construct the CNFS. The structure learning is based on the fuzzy similarity measure to determine the number of fuzzy rules, and the parameter learning is based on backpropagation algorithm to adjust the parameters. The simulation results have shown that (1) the CNFS model converges quickly and (2) the CNFS model has a lower root mean square (RMS) error than other models.     

An adaptive neuro-fuzzy inference system for predicting the risks of low back disorders due to manual material lifting jobs

Low back disorders (LBDs) due to manual material lifting tasks have become a significant issue which affects the quality of life of industrial population of workers in the U.S. and has enormous economic impact. For the last three decades researchers have been trying to understand the phenomena of LBDs and develop practical guidelines which could prevent these injuries from happening or limit the severity of these injuries after they have already occurred. One of the research streams concentrated on creating and testing various classification models based on a landmark Marras data set. The goal of these models was to categorize manual lifting jobs as low risk or high risk with respect to LBDs. This paper summarizes and critiques the previous approaches as some of them yielded unrealistically high classification accuracy rates. The paper also proposes an adaptive neuro-fuzzy inference system (ANFIS) to classify tasks into high risk or low risk. To our best knowledge ANFIS has not been used in this context yet and has not been used for classification of a binary target variable. The paper also compares the classification performances of the different parameters or configurations of ANFIS. The ANFIS model appears to be a viable option for risk classification as it exhibits the classification accuracy rates consistent with several previous studies. More importantly ANFIS generates easy to interpret control surfaces, membership functions, and fuzzy rules, thus allowing one to get a deeper insight into the relationships between risk factors which interact with each other in a complex and nonlinear way. Such insights could prove to be very useful for the much needed efforts to better understand LBDs.     

Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.     

Two neuro-fuzzy control schemes for a traffic-regulating buffer in wireless technology

Bluetooth wireless operates in 2.4-GHz Industrial Scientific and Medicine (ISM) frequency, which may interfere with other devices functioning within the same frequency band, such as WiFi. Furthermore, Moving Picture Expert Group (MPEG) variable bit rate (VBR) video demands larger and more stable bandwidth and may cause data loss and time delay as a result of the high variation in bit rate in Bluetooth channels with limited bandwidth. To address these issues, two new neuro-fuzzy schemes are developed to control the input and output of a buffer referred to here as the traffic-regulating buffer. Regarding the input of this buffer, a rule-based fuzzy scheme is introduced and supervised by a neural network technique as a master controller to monitor the arrival rate to the buffer. The output of the traffic-regulating buffer is observed by another rule-based fuzzy scheme and is supervised by a second neural network to monitor the departure rate. Computer simulation results demonstrate that the two proposed neuro-fuzzy models reduce standard deviation and excessive data loss, and they also show an improved picture quality as compared with conventional MPEG VBR video over a Bluetooth channel.     

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment.     

基于数据的间歇过程时变神经模糊模型研究

间歇过程的优化控制往往依赖于过程精确的数学模型,快速反应的市场要求使得数据驱动的建模方法被应用到了间歇过程的建模中.但常规的数据驱动建模方法在模型结构中没有考虑间歇过程具有重复性的特性,只是简单地将间歇过程作为一般的非线性结构进行处理.针对该问题,本文提出一种新颖的间歇过程时变神经模糊模型,将时间轴和批次轴的信息统一在...     

A method for particle location and field interpolation on complex, three-dimensional computational meshes

A method for locating particles within arbitrary three-dimensional computational meshes is described. It is based on an iterative procedure which uses transformed coordinates defined by iso-parametric functions. The method also enables one to interpolate field values from the mesh nodes to the particle position. Example applications demonstrate how effective the method is. For very distorted computational cells special practices have to be introduced in order to keep the number of iterations to a minimum.     

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

Characterization of polymer inks for drop-on-demand printing systems

This paper presents a detailed study on the properties of different polymer inks based on poly(3,4ethylenedioxythiophene)/polystyrenesulfonate regarding their processability in an experimental piezo driven drop-on-demand (DoD) micro-feeding system. Based on the rheological properties of the used inks and the mechanical properties of the printing system characteristic values are derived which allow to predict the processability of polymer inks in a given printing system. Beside the printability the influence of different polymer inks on the electrical characteristics of printed organic field effect transistors is investigated.     

光电轴角编码器的细分误差快速测量系统

为了在野外快速准确地测量编码器的误差,研制了编码器的误差测量系统。因为编码器的误差主要来自细分误差,该系统主要对细分误差进行分析。系统采用2片MAX125 A/D转换芯片对编码器输出的信号进行采集,通过并口将数据传给计算机进行误差分析。与传统的误差测量系统相比具有测量速度快、便携、简单等特点。利用该系统对某21位编码器的误差进行测量分析,证明该方法可行。     

含有色噪声的神经模糊Hammerstein模型分离辨识

针对实际工业过程中普遍存在的有色噪声,本文提出一种基于递推增广最小二乘算法的神经模糊Hammerstein模型辨识方法,突破了传统的Hammerstein模型迭代分离算法.首先,利用多信号源实现Hammerstein模型中静态非线性环节和动态线性环节的分离,大大简化了辨识过程,提高了串联环节参数的分离精度.其次,利用长除法将噪声模型用有限脉冲响应模型逼近,采用增广递推最小二乘法进行线性环节的参数估计.最后,采用神经模糊模型拟合静态非线性环节,同时设计了神经模糊模型参数的非迭代优化算法,改善了模型的使用范围.该方法保证了模型的预测精度,对含有色噪声的非线性系统具有较好的拟合效果.仿真结果验证了上述方法的有效性.     

Recursive decision directed estimation of reflection coefficients for seismic data deconvolution

This paper concerns the problem of estimation of the location and intensity of reflections of a seismic wavelet. A recursive maximum a posteriori probability (MAP) algorithm is derived as an alternative to the maximum likelihood (ML) algorithm of Mendel and Kormylo. The MAP approach proposed here yields a suboptimal detector which is substantially different in details from the corresponding approximate ML detector of Mendel and Kormylo. Simulation studies are presented to show that the MAP detector performs as well as the ML detector and can yield comparable results with much less computational effort. A comparative study of both the MAP and ML detectors has been made via simulations which show some interesting differences in structure as well as performance.     

基于Hermite 插值的多体系统动力学离散变分方法

针对多体系统动力学数值仿真问题,研究基于Hermite插值的离散变分方法.首先对广义坐标和广义速度进行Hermite插值,结合Gauss数值积分方法,利用Hamilton原理和离散力学变分原理,建立了含已知导数信息和含未知导数信息的Hermite插值离散变分数学模型,求解得到精确度较高的动力学仿真结果.该方法可以在步长较大时精确保持约束方程,并保持系统总能量在一定范围内有界变化,适用于长时间仿真情况.     

A modified gradient-based neuro-fuzzy learning algorithm and its convergence

Neuro-fuzzy approach is known to provide an adaptive method to generate or tune fuzzy rules for fuzzy systems. In this paper, a modified gradient-based neuro-fuzzy learning algorithm is proposed for zero-order Takagi-Sugeno inference systems. This modified algorithm, compared with conventional gradient-based neuro-fuzzy learning algorithm, reduces the cost of calculating the gradient of the error function and improves the learning efficiency. Some weak and strong convergence results for this algorithm are proved, indicating that the gradient of the error function goes to zero and the fuzzy parameter sequence goes to a fixed value, respectively. A constant learning rate is used. Some conditions for the constant learning rate to guarantee the convergence are specified. Numerical examples are provided to support the theoretical findings.