Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network based Fuzzy Inference System (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and forgetting factor recursive least square (FFRLS) for training the conclusion part. Two famous training algorithms for ANFIS are the gradient descent (GD) to update antecedent part parameters and using GD or recursive least square (RLS) to update conclusion part parameters. Lyapunov stability theory is used to study the stability of the proposed algorithms. This paper, also studies the stability of PSO as an optimizer in training the identifier. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data. Also, stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.     

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.     

Estimation of adaptive neuro-fuzzy inference system parameters with the expectation maximization algorithm and extended Kalman smoother

In this paper, we propose a new supervised learning method for adaptive neuro-fuzzy inference system (ANFIS) training, which uses the expectation maximization (EM) algorithm and extended Kalman smoother (EKS) together; we refer to it here as the EM-EKS training method. While the EKS tunes the ANFIS parameters, the EM algorithm estimates the parameters of the Kalman filter and avoids non-optimal performance. Besides, we also propose a new algorithm to select the initial values of the EKS parameters. We compare the EM-EKS method of ANFIS training with traditional ANFIS training. Although the new training method requires more computing time, it yields improved RMSE values in function approximation and prediction problems. Examples of benchmark function approximation and prediction illustrate the effectiveness of the EM-EKS ANFIS training method.     

Nonlinear modeling of a SOFC stack based on ANFIS identification

An adaptive neural-fuzzy inference system (ANFIS) model is developed to study different flows effect on the performance of solid oxide fuel cell (SOFC). During the process of modeling, a hybrid learning algorithm combining backpropagation (BP) and least squares estimate (LSE) is adopted to identify linear and nonlinear parameters in the ANFIS. The validity and accuracy of modeling are tested by simulations and the simulation results reveal that the obtained ANFIS model can efficiently approximate the dynamic behavior of the SOFC stack. Thus it is feasible to establish the model of SOFC stack by ANFIS.     

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.     

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.     

应用自适应神经模糊推理系统(ANFIS)进行建模与仿真

模糊规划的提取和隶属度函数的学习是模糊推理系统设计中重要而困难的问题，自适应神经模糊推理系统（ANFIS）方法基于Sugeno模糊模型，其结构类似于神经网络，采用反向传播算法和最小二乘法调整模糊推理系统的参数，并能自动产生模糊规划，本文应用该方法给出了对一个典型系统建模的仿真实例，取得了良好的效果。     

Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.     

基于IQPSO-GA优化ANFIS模型的服务器故障预警方法

针对服务器底层部分业务类硬件故障对系统稳定运行的影响,提出一种改进的量子行为粒子群优化(IQPSO)与遗传算法(GA)相结合的混合元启发式优化算法对自适应神经模糊推理系统(ANFIS)参数进行训练,以获得更准确的ANFIS规则进行硬件故障预警的方法。首先,通过分析服务器业务与硬件相关参数之间的映射关系,通过采集的数据集对ANFIS模型进行训练构造预测模型;其次,考虑ANFIS在梯度计算过程中存在容易陷入局部最优值的问题,设计了一种IQPSO算法结合GA中的交叉和变异算子操作混合元启发算法全局搜索ANFIS规则参数;最后,通过一组后处理样本数据集对所提方法有效性和稳定性进行了检验。实验结果表明,该方法可有效预警服务器硬件故障,基于所提混合元启发优化算法获得的ANFIS模型具备更快的收敛速度和更高的全局搜索精度,与传统ANFIS模型相比泛化精度提高了47%以上。     

基于自适应神经模糊推理系统的噪声消除方法

针对信号处理领域噪声消除的实际问题,提出了一种基于模糊推理的自适应神经网络控制方法.通过自适应神经模糊推理系统(ANFIS)对非线性系统的结构和参数进行辨识与自学习,采用混合学习算法,对前向参数和结论参数分别辨识,在提高精度的同时可加快训练收敛的速度,使控制系统具有良好动静态性和鲁棒性,实现了消除通信系统中噪声的目标,最后对基于ANFIS的噪声消除系统进行了建模和仿真,并与自适应神经网络滤波方法的结果对比,其结果证明了该方法的有效性.     

A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.     

A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger–Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation.     

自适应模糊神经网络在噪声消除中的应用

本文提出了一种基于模糊神经网络的噪声自适应消除方法,介绍了该方法的原理及实现算法,并利用特殊函数和一定的噪声作为样本信号,建立了基于模糊神经网络的自适应噪声消除模型。通过该模型对有用信号的参数进行了辨识,仿真结果表明该方法具有学习速度快、诊断精度高等优点,可用于通信线路及其他电子设备的噪声消除。     

基于ANFIS的焦炉火道温度预报模型研究

针对焦炉生产过程中直接检测火道温度成本高、精度低等问题,提出运用自适应神经网络模糊推理系统理论(ANFIS)建立焦炉火道温度预报模型,模型采用模糊减法聚类方法选取模糊规则数目,大大减少规则冗余量;结合最小二乘和误差反向传播混合算法对神经网络参数进行优化,采用现场的热工数据作为输入,将获得的模型与传统的线性回归模型和BP神经网络模型进行了比较,数值仿真结果表明所建立的模型具有学习速度快、预报精度高、泛化能力强等优点.     

An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm

The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects from the software service outsourcing perspective. The main aim of this study is to evaluate the GSD team-level service climate and GSD project outcome relationship based on adaptive neuro-fuzzy inference system (ANFIS) with the genetic learning algorithm. For measuring the team-level service climate, the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA) is adopted in the ANFIS, which is more appropriate to determine the optimal premise and consequent constructs by reducing the root-mean-square-error (RMSE) of service climate criteria. For measuring the GSD team-level service climate, synthesizing the literature reviews and consistent with the earlier studies on IT service climate which is classified into three main criterion: managerial practices (deliver quality of service), global service climate (measure overall perceptions), service leadership (goal setting, work planning, and coordination) which comprises 25 GSD team-level service climate attributes. The experimental results show that the optimal prediction error is obtained by the HTGLA-based ANFIS approach is 3.26%, which outperforms the earlier result that is the optimal prediction errors 4.41% and 5.75% determined, respectively, by ANFIS and statistical methods.     

基于T-S模型的锌钡白干燥煅烧过程自适应神经模糊推理系统建模

针对锌钡白干燥煅烧过程建模难的问题，提出了一种基于T-S模型的自适应神经模糊推理系统(ANFIS)建模方法．通过对模糊辨识系统的结构辨识和参数辨识，使网络自主、迅速地收敛到要求的输入输出关系．文章讨论了该网络的结构和学习算法，并通过仿真研究得出其良好的实际应用价值．     

A novel approach for ANFIS modelling based on full factorial design

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input–output data pairs of the system under consideration. The size of the input–output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.     

Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on-off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.     

Optimization of ANFIS with GA and PSO estimating α ratio in driven piles

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (I_d), embedment ratio (L/D), both of the pile end resistance (q_c) and base resistance at relatively 10% base settlement (q_b0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R², RMSE, and VAF. According to R², RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts.

     

基于自适应模糊网络的在线辨识

研究了基于一阶Sugeno的自适应网络模糊推理系统（ANFIS）进行在线辨识的方法。给出了该自适应网络的结构,在此基础上给出了网络权值的修正算法,即综合最陡下降法和最小二乘法得到的一种混合学习算法。对一个非线性模型进行了数字仿真,得到的在线辨识的结果优于采用反传算法的普通神经网络辨识方法。由此证明,一阶Sugeno模糊推理模型和混合学习算法的采用,使得该辨识方法具备网络结构简单、收敛速度快的优势,便于工程实现。