Fuzzy Min-Max Research Articles

Transfer learning using the online Fuzzy Min–Max neural network

In this paper, we present an empirical analysis on transfer learning using the Fuzzy Min---Max (FMM) neural network with an online learning strategy. Three transfer learning benchmark data sets, i.e., 20 Newsgroups, WiFi Time, and Botswana, are used for evaluation. In addition, the data samples are corrupted with white Gaussian noise up to 50 %, in order to assess the robustness of the online FMM network in handling noisy transfer learning tasks. The results are analyzed and compared with those from other methods. The outcomes indicate that the online FMM network is effective for undertaking transfer learning tasks in noisy environments.

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.

Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.

Fuzzy Based Multi-Objective Evolutionary Algorithm for Optimal Location of FACTS Devices in a Power System

This paper presents a Multi Objective Evolutionary Algorithm (MOEA) to solve nonlinear power system optimization problem. The optimization is done with three variables; choice, location and rating of FACTS devices. The targeted technical objectives are; to control the power flow, increase the transmission line capability to its maximum thermal limits, to keep the voltages within limits while minimizing losses. The parameters of objectives are optimized independently and then the formulation of a nonlinear constrained multi-objective optimization problem is done while carrying out concurrent optimization taking two and three objectives. A Fuzzy min max approach is incorporated to reduce the best solution out of non dominated Pareto optimal set. Assessments have been done on IEEE 14 and IEEE 30 bus system for different loading conditions with two devices SVC and TCSC modeled in steady state and the results affirm the potency of the propound approach.

FMM 신경망에서 연관도요소를 이용한 규칙 추출 기법

본 연구에서는 수정된 구조의 FMM 신경망으로부터 패턴 인식을 위한 규칙 추출 방법을 제안한다. 제안된 방법은 학습데이터에서 특징값에 대한 빈도 요소를 반영하는 하이퍼박스 정의를 기반으로 하는데, 이로부터 특징과 패턴클래스 간의 상호 연관도 요소를 정의 하였다. 이는 기존의 모델에서 사용되는 하이퍼박스 중첩테스트 및 축소(contraction) 기법을 사용하지 않아도 하이퍼박스의 중첩에 의한 분류의 모호성을 해결할 수 있게 한다. 본 연구에서는 패턴 클래스의 각 차원별로 퍼지 분할을 기반으로 하는 수정된 하이퍼박스 멤버쉽 함수와 이를 사용하는 학습방법을 제시한다. 제안된 기법으로부터 특정패턴의 분류를 위한 자극성(excitatory) 특징 및 억제성(inhibitory) 특징을 구분하고 이들 정보는 규칙 생성과정에 적용된다. 수화 인식에 관한 실험에 제안된 방법론을 적용함으로써 제안된 이론의 타당성을 실험적으로 고찰하였다. In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

Fuzzy Mean Point Clustering using K-means algorithm for implementing the movecentroid function code

The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN. Here we have provided movecentroid’s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done. In the experiment we have used four datasets and observed results in nano seconds there is huge difference in output as time is reduced for Fuzzy Min-Max code execution of fuzzy calculations of clustering.

Preterm Birth Prediction Using Cuckoo Search-Based Fuzzy Min-Max Neural Network

In the latest history, a Decision making and prediction system has been investigated vigorously for several decades and has got a lift. Together with preterm birth study, the decision support system has been explored in different areas. Using fuzzy, neural network and cuckoo search algorithm, the medical decision support system is improved for the forecast of preterm birth in this document. A two-module pattern categorization and rule extraction system has been highlighted by this study, where in the former module emphasises an altered fuzzy min–max (FMM) neural-network-based pattern classifier, whereas the subsequent module emphasises oppositional cuckoo search based rule extractor. With the theory of opposition, this paper examines altered cuckoo search algorithm. Using Pre Term Birth (PTB) datasets, the empirical analysis is executed and applied using MATLAB. Performance assessment matrix occupied is the precision and our suggested method is compared with the active methods. It is examined that our suggested method has attained improved precision value (85.6 %) when compared to FMM (77.36 %) which illustrates the efficiency of the suggested method from the results

Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors

In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min–max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

A novel trust measurement method based on certified belief in strength for a multi-agent classifier system

A novel trust measurement method, namely, certified belief in strength (CBS), for a multi-agent classifier system (MACS) is proposed in this paper. The CBS method aims to improve the performance of the constituent agents of the MACS, viz., the fuzzy min–max (FMM) neural network classifier. Trust measurement is accomplished using reputation and strength of the constituent agents. Trust is built from strong elements that are associated with the FMM agents, allowing the CBS method to improve the performance of the MACS. An auction procedure based on the sealed bid, namely, the first price method, is adopted for the MACS in determining the winning agent. The effectiveness of the CBS method and the bond (based on trust) is verified by using a number of benchmark data sets. The results demonstrate that the proposed MACS-CBS model is able to produce better accuracy and stability as compared with those from other existing methods.

Offline Signature Verification using FMCN with GA based Optimization of Features

In recent years, along with extraordinary diffusion of internet and growing need of personal identification in many applications, signature verification is considered with interest. This paper proposed an offline signature verification method based on Genetic Algorithm and Fuzzy Min Max Neural Network Classifier with Compensatory Neuron. The proposed method is basically consists of two steps. At first step optimizing the features using genetic algorithm, and at second step signature recognition is done using Fuzzy Min Max Neural Network Classifier with Compensatory Neurons. The sample of signatures is used to represent a particular person. The sample signature is first preprocessed, and then features of the processed signature are extracted by using Krawtchouk moment. After feature extraction, these features are optimized by using genetic algorithm and finally optimized features are given to the classification phase for recognition. With this proposed method, we get the 98% accuracy in recognition and less time is required for classification with optimized features as compared to time required for classification without optimizing feature.

축산생육환경 유해가스 모니터링을 위한 무선가스측정시스템 개발

축산생육환경에서 다양한 경로로부터 발생하는 유해가스는 가축 및 농가작업자에게 직/간접 적으로 영향을 미칠 수 있으며, 점차적인 사육조밀화와 동절기 밀폐환경에 장기간 노출 시 치명적일 수 있다. 본 논문에서는 가축분뇨로부터 발생하는 암모니아, 황화수소, 휘발성유기화합물 가스 등을 모니터링하기 위해 무선가스센서노드와 퍼지 최소-최대 신경회로망을 이용한 가스인식 소프트웨어로 이루어진 가스측정시스템을 제안한다. 제안한 시스템의 성능을 평가하기 위해 가스측정실험환경을 구축하여 제작한 무선가스센서노드로 가스측정실험을 수행하고, 개발한 가스인식 소프트웨어로 대상가스 분류시험을 통해 성능을 입증한다.

Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection

This paper presents an efficient and effective decision support system (DSS) for operational monitoring and control of water distribution systems based on a three layer General Fuzzy Min–Max Neural Network (GFMMNN) and graph theory. The operational monitoring and control involves detection of pipe leakages. The training data for the GFMMNN is obtained through simulation of leakages in a water network for a 24h operational period. The training data generation scheme includes a simulator algorithm based on loop corrective flows equations, a Least Squares (LS) loop flows state estimator and a Confidence Limit Analysis (CLA) algorithm for uncertainty quantification entitled Error Maximization (EM) algorithm. These three numerical algorithms for modeling and simulation of water networks are based on loop corrective flows equations and graph theory. It is shown that the detection of leakages based on the training and testing of the GFMMNN with patterns of variation of nodal consumptions with or without confidence limits produces better recognition rates in comparison to the training based on patterns of nodal heads and pipe flows state estimates with or without confidence limits. It produces also comparable recognition rates to the original recognition system trained with patterns of data obtained with the LS nodal heads state estimator while being computationally superior by requiring a single architecture of the GFMMNN type and using a small number of pattern recognition hyperbox fuzzy sets built by the same GFMMNN architecture. In this case the GFMMNN relies on the ability of the LS loop flows state estimator of making full use of the pressure/nodal heads measurements existent in a water network.

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Data-Core-Based Fuzzy Min–Max Neural Network for Pattern Classification

A fuzzy min-max neural network based on data core (DCFMN) is proposed for pattern classification. A new membership function for classifying the neuron of DCFMN is defined in which the noise, the geometric center of the hyperbox, and the data core are considered. Instead of using the contraction process of the FMNN described by Simpson, a kind of overlapped neuron with new membership function based on the data core is proposed and added to neural network to represent the overlapping area of hyperboxes belonging to different classes. Furthermore, some algorithms of online learning and classification are presented according to the structure of DCFMN. DCFMN has strong robustness and high accuracy in classification taking onto account the effect of data core and noise. The performance of DCFMN is checked by some benchmark datasets and compared with some traditional fuzzy neural networks, such as the fuzzy min-max neural network (FMNN), the general FMNN, and the FMNN with compensatory neuron. Finally the pattern classification of a pipeline is evaluated using DCFMN and other classifiers. All the results indicate that the performance of DCFMN is excellent.

Unsupervised Data Classification for Convex and Non Convex Classes

We present in this work, a new unsupervised data classification technique based on a three steps system: Split, Clean and M erge. In this system, the classes are represented by a set of subclasses that we call prototypes. The prototypes are created in an incremental way from the initial data set. No prior knowledge on the classes is required. The data are presented to the system one by one in an arbitrary way. The system built on a neural network strategy ends up by acquiring knowledge on the data and gathers the data into a set of real classes which may have a non convex structure. The method proposed is compared to the fuzzy C-means ‘FCM’ and fuzzy min max clustering ‘FMMC’ methods through a number of simulations. The results obtained by the proposed method are very good.

Autonomous and adaptive procedure for cumulative failure prediction

An autonomous adaptive reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available failure time data, Fuzzy Min–Max algorithm is used to globally optimize the number of the k Gaussian nodes. This technique allows determining and initializing the k-centers of the neural network architecture in an iterative way. The user does not have to define arbitrary some parameters. The optimized neural network architecture is then iteratively and dynamically reconfigured as new failure occurs. The performance of the proposed approach has been tested using sixteen real-time software failure data.

Fuzzy min–max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.

Fuzzy classification in dynamic environments

The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min–max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches.

A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ?don't care? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.