Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system

Recently, significant of the robust texture image classification has increased. The texture image classification is used for many areas such as medicine image processing, radar image processing, etc. In this study, a new method for invariant pixel regions texture image classification is presented. Wavelet packet entropy adaptive network based fuzzy inference system (WPEANFIS) was developed for classification of the twenty 512 × 512 texture images obtained from Brodatz image album. There, sixty 32 × 32 image regions were randomly selected (overlapping or non-overlapping) from each of these 20 images. Thirty of these image regions and other 30 of these image regions are used for training and testing processing of the WPEANFIS, respectively. In this application study, Daubechies, biorthogonal, coiflets, and symlets wavelet families were used for wavelet packet transform part of the WPEANFIS algorithm, respectively. In this way, effects to correct texture classification performance of these wavelet families were compared. Efficiency of WPEANFIS developed method was tested and a mean %93.12 recognition success was obtained.     

An expert system for speaker identification using adaptive wavelet sure entropy

In this study, an expert speaker identification system is presented for speaker identification using Turkish speech signals. Here, a discrete wavelet adaptive network based fuzzy inference system (DWANFIS) model is used for this aim. This model consists of two layers: discrete wavelet and adaptive network based fuzzy inference system. The discrete wavelet layer is used for adaptive feature extraction in the time–frequency domain and is composed of discrete wavelet decomposition and discrete wavelet entropy. The performance of the used system is evaluated by using repeated speech signals. These test results show the effectiveness of the developed intelligent system presented in this paper. The rate of correct classification is about 90.55% for the sample speakers.     

Interest point based face recognition using adaptive neuro fuzzy inference system

Multimedia Tools and Applications - In this paper, an efficient face recognition method using AGA and ANFIS-ABC has been proposed. At first stage, the face images gathered from the database are...     

Hierarchical wavelet packet fuzzy inference system for pattern classification and system identification

This study presents a hierarchical Takagi–Sugeno–Kang type fuzzy system called hierarchical wavelet packet fuzzy inference system. In the proposed method, wavelet packet transform is applied on the input data to produce approximation and detail sub-bands of the input data and the output is used as the input vector of the proposed network. This network uses a hierarchical structure same as wavelet packet decomposition tree, in which adaptive network-based fuzzy inference system is used as sub-model. Also, gradient descent algorithm is chosen for training the parameters of antecedent and conclusion parts of the sub-models. In order to evaluate the capability of the proposed method, its applications in pattern classification, system identification and time-series prediction have been studied. The results show that the proposed method performs better than the other conventional models.     

GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system

Churn management is important and critical issue for Global Services of Mobile Communications (GSM) operators to develop strategies and tactics to prevent its subscribers to pass other GSM operators. First phase of churn management starts with profile creation for the subscribers. Profiling process evaluates call detail data, financial information, calls to customer service, contract details, market details and geographic and population data of a given state. In this study, input features are clustered by x-means and fuzzy c-means clustering algorithms to put the subscribers into different discrete classes. Adaptive Neuro Fuzzy Inference System (ANFIS) is executed to develop a sensitive prediction model for churn management by using these classes. First prediction step starts with parallel Neuro fuzzy classifiers. After then, FIS takes Neuro fuzzy classifiers’ outputs as input to make a decision about churners’ activities.     

基于HAAR小波的分级说话人辨识

从线性预测（LP）残差信号中提出了一种新的特征提取方法,这种特征跟单个的说话人的声道密切相关。通过把HAAR小波变换运用于LP 残差而获得了一个新的特征（HOCOR）。为了进一步提高系统的鲁棒性和辨识率,在采用分级说话人辨识的基础上,将基音周期的高斯概率密度对GMM分类器的似然度进行加权,形成新的似然度进行说话人辨识。试验结果显示,所提出系统的鲁棒性和辨识率都有所提高。     

Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part

This paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFWNAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. All parameters of the consequent parts are updated online based on Direct Adaptive Control Theory (DACT) and employing a back-propagation-based approach. The stabilizer initialization is performed using an approach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithm is also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of the proposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirement to use any identification process. Kundur's four-machine two-area benchmark power system and six-machine three-area power system are used with the aim of assessing the effectiveness of the proposed stabilizer. The results are promising and show that the inter-area oscillations are successfully damped by the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEE standard multi-band power system stabilizer (MB-PSS), and the conventional PSS.     

Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets

A correct diagnosis of tuberculosis disease can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution that makes diagnosis of tuberculosis as accurate as possible and helps deciding whether it is reasonable to start tuberculosis treatment on suspected patients without waiting for the exact medical test results. We proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. Data set collected from 503 different patient records which are obtained from a private health clinic (consent of physicians and patients). Patient record has 30 different attributes which covers demographical and medical test data. ANFIS model was generated by using 250 records. Also, rough set method was implemented by using the same data set. The ANFIS model classifies the instances with correctness of 97 %, whereas rough set algorithm does the same classification with correctness of 92 %. This study has a contribution on forecasting patients before the medical tests.     

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

基于自适应神经网络模糊推理系统的蓄电池SOH预测

针对装甲车辆铅酸蓄电池健康状况影响因素复杂、难以准确预测的特点,提出了基于自适应神经网络模糊推理系统的蓄电池SOH预测模型。在确定模型的输入变量后,对其进行了MATLAB仿真和实测数据验证分析。结果表明,该模型具有很高的预测精度,在装甲车辆铅酸蓄电池SOH预测上具有很高的实用价值。     

Estimation of cementation factor in carbonate reservoir by using genetic fuzzy inference system

Neural Computing and Applications - Water saturation is a key parameter in reservoir engineering to calculate the volume of hydrocarbon in reservoirs. The first attempt to estimate water saturation...     

采用遗传算法的文本无关说话人识别

为解决在说话人识别方法的矢量量化(Vector Quantization,VQ)系统中,K-均值法的码本设计很容易陷入局部最优,而且初始码本的选取对最佳码本设计影响很大的问题,将遗传算法(Genetic Algorithm,GA)与基于非参数模型的VQ相结合,得到1种VQ码本设计的GA-K算法.该算法利用GA的全局优化能力得到最优的VQ码本,避免LBG算法极易收敛于局部最优点的问题;通过GA自身参数,结合K-均值法收敛速度快的优点,搜索出训练矢量空间中全局最优的码本.实验结果表明,GA-K算法优于LBG算法,可以很好地协调收敛性和识别率之间的关系.     

Multimodal speaker identification using an adaptive classifier cascade based on modality reliability

We present a multimodal open-set speaker identification system that integrates information coming from audio, face and lip motion modalities. For fusion of multiple modalities, we propose a new adaptive cascade rule that favors reliable modality combinations through a cascade of classifiers. The order of the classifiers in the cascade is adaptively determined based on the reliability of each modality combination. A novel reliability measure, that genuinely fits to the open-set speaker identification problem, is also proposed to assess accept or reject decisions of a classifier. A formal framework is developed based on probability of correct decision for analytical comparison of the proposed adaptive rule with other classifier combination rules. The proposed adaptive rule is more robust in the presence of unreliable modalities, and outperforms the hard-level max rule and soft-level weighted summation rule, provided that the employed reliability measure is effective in assessment of classifier decisions. Experimental results that support this assertion are provided.     

基于小波隶属函数的模糊推理规则优化

隶属函数决定着模糊集的特征,建立小波基函数与隶属函数之间的联系,从而利用小波分析探讨模糊推理的实质,以一种非对称Haar小波基与三角型、梯型隶属函数的对应关系为基础,将小波分析、遗传算法与模糊系统结合,利用遗传算法实现小波隶属函数的训练学习,进而实现模糊推理规则的优化。     

基于小波隶属函数的模糊推理规则优化

隶属函数决定着模糊集的特征，建立小波基函数与隶属函数之间的联系，从而利用小波分析探讨模糊推理的实质，以一种非对称Haar小波基与三角型、梯型隶属函数的对应关系为基础，将小波分析、遗传算法与模糊系统结合，利用遗传算法实现小波隶属函数的训练学习，进而实现模糊推理规则的优化。     

Wavelet fuzzy LVQ based speaker verification system

This work evaluates the performance of speaker verification system based on Wavelet based Fuzzy Learning Vector Quantization (WLVQ) algorithm. The parameters of Gaussian mixture model (GMM) are designed using this proposed algorithm. Mel Frequency Cepstral Coefficients (MFCC) are extracted from the speech data and vector quantized through Wavelet based FLVQ algorithm. This algorithm develops a multi resolution codebook by updating both winning and nonwinning prototypes through an unsupervised learning process. This codebook is used as mean vector of GMM. The other two parameters, weight and covariance are determined from the clusters formed by the WLVQ algorithm. The multi resolution property of wavelet transform and ability of FLVQ in regulating the competition between prototypes during learning are combined in this algorithm to develop an efficient codebook for GMM. Because of iterative nature of Expectation Maximization (EM) algorithm, the applicability of alternative training algorithms is worth investigation. In this work, the performance of speaker verification system using GMM trained by LVQ, FLVQ and WLVQ algorithms are evaluated and compared with EM algorithm. FLVQ and WLVQ based training algorithms for modeling speakers using GMM yields better performance than EM based GMM.     

Hypoglycaemia detection using fuzzy inference system with multi-objective double wavelet mutation Differential Evolution

In this paper, a fuzzy inference system (FIS) is developed to recognize hypoglycaemic episodes. Hypoglycaemia (low blood glucose level) is a common and serious side effect of insulin therapy for patients with diabetes. We measure some physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The FIS captures the relationship between the inputs of heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QT_c), change of HR, change of QT_c and the output of hypoglycaemic episodes to perform the classification. An algorithm called Differential Evolution with Double Wavelet Mutation (DWM-DE) is introduced to optimize the FIS parameters that govern the membership functions and fuzzy rules. DWM-DE is an improved Differential Evolution algorithm that incorporates two wavelet-based operations to enhance the optimization performance. To prevent the phenomenon of overtraining (over-fitting), a validation approach is proposed. Moreover, in this problem, two targets of sensitivity and specificity should be met in order to achieve good performance. As a result, a multi-objective optimization using DWM-DE is introduced to perform the training of the FIS. Experiments using the data of 15 children with TIDM (569 data points) are studied. The data are randomly organized into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). The result shows that the proposed FIS tuned by the multi-objective DWM-DE can offer good performance of doing classification.     

基于模糊推理的适应性自动谈判系统研究

提出了一个基于模糊规则推理的自动谈判系统.针对当前自动谈判系统中主观估计对手偏好信息、忽略对手报价历史,不能响应谈判环境变化的问题,采用贝叶斯学习对手款项权重,遗传算法优化模糊规则中的参数.实例计算分析表明该系统具有一定的可行性和有效性.     

Feedforward neural network and adaptive network-based fuzzy inference system in study of power lines

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This paper presents novel approach based on the use of both feedforward neural network (FNN) and adaptive network-based fuzzy inference system (ANFIS) to estimate electric and magnetic fields around an overhead power transmission lines. An FNN and ANFIS used to simulate this problem were trained using the results derived from the previous research. It is shown that proposed approach ensures satisfactory accuracy and can be a very efficient tool and useful alternative for such investigations.