基于GA-ANFIS的装甲车辆蓄电池SOH预测方法

为保持装甲车辆的机动安全和运行可靠,提高其铅酸蓄电池健康状态的预测能力至关重要。本文将遗传算法与自适应模糊神经系统相结合,提出了一种基于GA-ANFIS的装甲车辆蓄电池SOH预测方法,着重分析了该方法的总体流程和训练过程。着眼装甲车辆的工作环境,在放电深度和输出能量的基础上,引入海拔和温度作为模型的输入。在Matlab的实验结果表明,GA-ANFIS相比ANFIS测试数据误差减小47.6%,四输入GA-ANFIS相比两输入GA-ANFIS测试数据误差减小51.2%,验证了方法的有效性。     

Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R²) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R² of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

     

Prediction of coefficient of consolidation in soil using machine learning techniques

Coefficient of consolidation in the soil is the significant engineering properties and an important parameter for designing and auditing of geo-technical structures. Therefore, in this study, authors have proposed an efficient methodology to prediction the coefficient of consolidation using machine learning models namely Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Adaptive Network based Fuzzy Inference System (ANFIS). Further, various feature selection techniques such as Least Absolute Shrinkage and Selection Operator algorithm (LASSO), Random Forests - Recursive Feature Elimination (RF-RFE), and Mutual information have also been applied. It has been observed that feature selection methods have enhanced the quality of prediction model by eliminating the irrelevant features and utilized only important features while building the prediction models. Experiments are performed on the dataset collected on the 534 soil samples from Ha Noi –Hai Phong highway project, Vietnam. Experimental results show the adequacy of the proposed model, and the hybrid approach ANFIS which is a fusion of ANN and fuzzy inference system includes complementary information of the uncertainty and adaptability. ANFIS along with LASSO feature selection method produces the coefficient of determination of 0.831 and thus provides the best prediction for the coefficient of consolidation of a soil as compared to other approaches.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy

In this study, the efficiency of neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the transfer length of prestressing strands in prestressed concrete beams was investigated. Many models suggested for the transfer length of prestressing strands usually consider one or two parameters and do not provide consistent accurate prediction. The alternative approaches such as GEP and ANFIS have been recently used to model spatially complex systems. The transfer length data from various researches have been collected to use in training and testing ANFIS and GEP models. Six basic parameters affecting the transfer length of strands were selected as input parameters. These parameters are ratio of strand cross-sectional area to concrete area, surface condition of strands, diameter of strands, percentage of debonded strands, effective prestress and concrete strength at the time of measurement. Results showed that the ANFIS and GEP models are capable of accurately predicting the transfer lengths used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.     

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.     

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.

     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

Numerical study on the feasibility of dynamic evolving neural-fuzzy inference system for approximation of compressive strength of dry-cast concrete

This paper assesses effectiveness of dynamic evolving neural-fuzzy inference system (DENFIS) models in predicting the compressive strength of dry-cast concretes, and compares their prediction performances with those of regression, neural network (NN) and ANFIS models. The results of this study emphasized capabilities of online first-order and offline high-order Takagi–Sugeno (TSK) type DENFIS models for prediction purposes, whereas offline first-order TSK-type DENFIS models did not produce reliable results. Comparison between the produced results of an elite high-order DENFIS model with those predicted by the selected NN, regression and ANFIS models showed effectiveness of DENFIS model than the regression model, while its performance was similar to or slightly better than the other artificial prediction tools.     

Predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment

Driving a car and piloting an airplane are the most common examples for manual control of complicated processes. Human operators are known to be nonlinear, adaptive, time varying and intelligent controllers. In some cases, the human operator may or may not be well trained or an expert, showing different dynamics from operator to operator as in driving example. Therefore, it is very difficult to obtain mathematical models of human operators in a human-in-the-loop-manual control tasks. The goal of this research is to find a simple dynamic model for the prediction of the human operator actions in a manual control system. A computer-based experiment has been designed using the system identification theory to collect data from human operators. The autoregressive with exogenous inputs (ARX), as a parametric model and the adaptive-network-based fuzzy inference system (ANFIS), as an intelligent modeling approach that has the advantages of both neural networks and fuzzy logic, have been investigated and compared for simple and fast implementation to predict the response of human operators. ANFIS, having only 32 rules, provided much better prediction results than ARX model.     

Neuro–fuzzy modeling tools for estimation of torque in Savonius rotor wind turbine

In the present paper, the ability and accuracy of an adaptive neuro–fuzzy inference system (ANFIS) has been investigated for dynamic modeling of wind turbine Savonius rotor. The main objective of this research is to predict torque performance as a function of the angular position of turbine. In order to better understanding the present technique, the dynamic performance modeling of a Savonius rotor is an important consideration for the wind turbine design procedure. It could be difficult to derive the exact mathematical derivation for the input–output relationships because of the complexity of the design algorithm. In order to show the best fitted algorithm, an extensive comparison test was applied on the ANFIS (adaptive neuro–fuzzy inference system), FIS (fuzzy inference system), and RBF (radial basis function). Resulting from the extensive comparison test, the ANFIS procedure yields very accurate results in comparison with two alternate procedures. The results show that there is an excellent agreement between the testing data (not used in training) and estimated data, with average errors very low. Also FIS with threshold 0.05 and the trained ANFIS are able to accurately capture the non-linear dynamics of torque even for a new condition that has not been used in the training process (testing data). For the sake of comparison, the results of the proposed ANFIS model is compared with those of the RBF model, as well. For implementation of the present technique, the Matlab codes and related instructions are efficiently used, respectively.     

A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process

Due to the controversy associated with modelling Electrical Discharge Machining (EDM) processes based on physical laws; this task is predominantly accomplished using empirical modelling methods. The modelling studies reported in the literature deal predominantly with quantitative parameters i.e. ones with numerical levels. In fact, modelling categorical parameters has been devoted a scant attention. This study reports the results of an EDM experiment conducted on the Ti–6Al–4V alloy. Its aim was to model the relationship between the Material Removal Rate (MRR) and the parameters of the process, namely, current, pulse on-time and pulse off-time along with a categorical factor (electrode material). The modelling process was accomplished using adaptive neuro-fuzzy inference system (ANFIS) and polynomial modelling approaches. In fact, one purpose of this study was to compare the performance of these modelling approaches as no study was found contrasting their prediction capability in the literature. Regarding the polynomial model, two numerical parameters (current and pulse on-time) were declared significant in the ANOVA together with the electrode material and its interaction with pulse on-time. Thus, they were all incorporated in the developed polynomial model. Furthermore, five ANFIS models with 6, 9, 19, 21 and 51 rules were developed utilizing the first order Sugeno fuzzy approach by back-propagation neural networks training algorithm. Of these, the ANFIS model with 21 rules was the best. This model also outperformed the polynomial model remarkably in terms of predicting error, residuals range and the correlation coefficient between the experimental and predicted MRR values. The study sheds light on the powerful learning capability of ANFIS models and its superiority over the conventional polynomial models in terms of modelling complex non-linear machining processes.     

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.     

An adaptive neuro‐fuzzy system for stock portfolio analysis

We propose an adaptive neuro‐fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection‐clustering algorithm. The neuro‐fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc.     

Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

In this paper, an adaptive network-based fuzzy inference system (ANFIS) with the genetic learning algorithm is used to predict the workpiece surface roughness for the end milling process. The hybrid Taguchi-genetic learning algorithm (HTGLA) is applied in the ANFIS to determine the most suitable membership functions and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root-mean-squared-error performance criterion. Experimental results show that the HTGLA-based ANFIS approach outperforms the ANFIS methods given in the Matlab toolbox and reported recently in the literature in terms of prediction accuracy.     

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.     

Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques

The purpose of this paper is to investigate the relationship between adverse events and infrastructure development investments in an active war theater by using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) where the accuracy of the predictions is directly beneficial from an economic and humanistic point of view. Fourteen developmental and economic improvement projects were selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded or hijacked, and the total number of adverse events has been estimated.The results obtained from analysis and testing demonstrate that ANN, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic project data. When the model accuracy was calculated based on the mean absolute percentage error (MAPE) for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results. For the purpose of allocating resources and developing regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater, with emphasis on predicting the occurrence of events. We conclude that the importance of infrastructure development projects varied based on the specific regions and time period.     

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R²) values were obtained from ANFIS-GA model. The values of R² and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.

     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R ²) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R ², RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.