Fuzzy Min-max Network Research Articles

A compact fuzzy min max network with novel trimming strategy for pattern classification

Hyperbox classifier has large contribution to the field of pattern classification, because of its efficiency and transparency. Hyperbox classifier is efficiently implemented by using fuzzy min–max (FMM) neural network. FMM was modified many times to improve the classification accuracy. Moreover, there still exists a space for increasing the accuracy of hyperbox based classifiers. In this paper, four modifications are proposed to FMM network for increasing the classification accuracy rate. First, centroid and K-highest (CCK) based criteria to select the expandable hyperbox. Second, a new set of overlap test cases to consider all types of overlapping regions. Third, a new set of contraction rules to settle the overlapped regions. Fourth, novel hyperbox trimming strategy to reduce the system complexity. The proposed method is compared with FMM, enhanced FMM (EFMM) and Kn_FMM using five datasets. Experimental results clearly reflect the improved efficiency of proposed method. Proposed FMM (PFMM) network is also used to classify the histopathological images for knowing the best magnifying factor.

A semisupervised learning model based on fuzzy min–max neural networks for data classification

Semisupervised learning (SSL) models are useful for undertaking classification problems with a small set of labeled samples and a large number of unlabeled samples. In this regard, the family of fuzzy min–max (FMM) neural networks offers the capability of online learning for addressing both unsupervised and supervised problems. As such, this paper proposes a novel two-stage SSL model based on FMM networks, denoted SSL–FMM. The first stage employs the unlabeled samples to generate a number of hyperboxes using the unsupervised FMM algorithm, while the second stage uses the labeled samples to associate the generated hyperboxes with their target classes using the supervised FMM algorithm. In addition, a neighborhood-labeling mechanism based on the Euclidean distance and hyperbox centroids is formulated to associate the unlabeled hyperboxes with the most likely target classes. A number of benchmark problems and a real-world case study are employed to evaluate the effectiveness of the proposed SSL–FMM model. The outcome indicates that SSL–FMM is able to use unlabeled samples effectively and improve the FMM performance, producing promising results compared with other SSL methods in the literature.

An Improved Fuzzy Min–Max Neural Network for Data Classification

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min–max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k -nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k- nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.

A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification

Swarm intelligence (SI)-based optimization methods have been extensively used to tackle feature selection problems. A feature selection method extracts the most significant features and removes irrelevant ones from the data set, in order to reduce feature dimensionality and improve the classification accuracy. This paper combines the incremental learning Fuzzy Min–Max (FMM) neural network and Brain Storm Optimization (BSO) to undertake feature selection and classification problems. Firstly, FMM is used to create a number of hyperboxes incrementally. BSO, which is inspired by the human brainstorming process, is then employed to search for an optimal feature subset. Ten benchmark problems and a real-world case study are conducted to evaluate the effectiveness of the proposed FMM-BSO. In addition, the bootstrap method with the 95% confidence intervals is used to quantify the results statistically. The experimental results indicate that FMM-BSO is able to produce promising results as compared with those from the original FMM network and other state-of-the-art feature selection methods such as particle swarm optimization, genetic algorithm, and ant lion optimization.

Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.

Multi-level fuzzy min-max neural network classifier.

In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output of the network is formed by combining the outputs of these classifiers. MLF is capable of learning nonlinear boundaries with a single pass through the data. According to the obtained results, the MLF method, compared to the other FMM networks, has the highest performance and the lowest sensitivity to maximum size of the hyperbox parameter (θ), with a training accuracy of 100% in most cases.

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.

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.

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.

A modified fuzzy min–max neural network with rule extraction and its application to fault detection and classification

The fuzzy min–max (FMM) network is a supervised neural network classifier that forms hyperboxes for classification and prediction. In this paper, we propose modifications to FMM in an attempt to improve its classification performance when a small number of large hyperboxes are formed in the network. Given a new input pattern, in addition to measuring the fuzzy membership function of the input pattern to the hyperboxes formed in FMM, an Euclidean distance measure is introduced for predicting the target class associated with the new input pattern. A rule extraction algorithm is also embedded into the modified FMM network. A confidence factor is calculated for each FMM hyperbox, and a user-defined threshold is used to prune the hyperboxes with low confidence factors. Fuzzy if– then rules are then extracted from the pruned network. The benefits of the proposed modifications are twofold, viz., to improve the performance of FMM when large hyperboxes are formed in the network; to facilitate the extraction of a compact rule set from FMM to justify its predictions. To assess the effectiveness of modified FMM, two benchmark pattern classification problems are experimented, and the results from different methods published in the literature are compared. In addition, a fault detection and classification problem with a set of real sensor measurements collected from a power generation plant is evaluated using modified FMM. The results obtained are analyzed and explained, and implications of the modified FMM network as a useful fault detection and classification tool in real environments are discussed.

Application of a Neural Fuzzy System with Rule Extraction to Fault Detection and Diagnosis

In this paper, the fuzzy min–max (FMM) neural network is integrated with a rule extraction algorithm, and the resulting network is applied to a real-world fault detection and diagnosis task in complex industrial processes. With the rule extraction capability, the FMM network is able to overcome the “black-box” phenomenon by justifying its predictions using fuzzy if–then rules that are comprehensible to domain users. To assess the effectiveness of the FMM network, real sensor measurements are collected and used for detecting and diagnosing the heat transfer and tube blockage conditions of a circulating water (CW) system in a power generation plant. The FMM network parameters are systematically varied and tested, with the results explained. Bootstrapping is employed to quantify stability of the network performance statistically. The extracted rules are found to be compatible with the domain information as well as the opinions of domain experts who are involved in the maintenance of the CW system. Implications of the FMM network with the rule extraction facility as an intelligent and useful fault detection and diagnosis tool are discussed.

CMOS IMPLEMENTATION OF A VOLTAGE-MODE FUZZY MIN-MAX CONTROLLER

In this paper, a general-purpose fuzzy min-max network using a Gaussian-type membership function fuzzifier is proposed. Particularly, CMOS implementations of the Gaussian-type membership function fuzzifier circuits, min-max operators, and the defuzzifier circuit are analyzed. Programmability of the proposed Gaussian-type function fuzzifier can be achieved by changing the gate voltages and the sizes of transistors in the differential pairs. A closed-loop control scheme is used between the fuzzifier and defuzzifier blocks to compensate the global normalization of the denominator in the division of a centroid calculation in the defuzzifier block.