Adaptive Fuzzy Clustering Algorithm Research Articles

Operational risk assessment for hydroelectric generating units using multi-head spatio-temporal attention and adaptive fuzzy clustering

A novel operational risk assessment method for hydroelectric generating units (HGUs) is presented in this article. First, a multi-head spatio-temporal attention gated network (MSTAGN) is proposed to establish an operation risk benchmark model for HGUs to reveal the intricate relationship between performance and its multiple influencing factors. In particular, MSTAGN learns complex interaction relationships among multiple influencing factors in both temporal and spatial dimensions and automatically extracts important features. Then, a nonlinear mapping function is constructed to extract the deviation of the current measured performance parameters from the predicted baseline performance parameters as the operation risk degree. On this basis, an adaptive fuzzy clustering algorithm is proposed to achieve a clear classification of the operating risk level for HGUs. The proposed method is applied in a HGU in Sichuan province, China. The results of comparative experiments demonstrate its viability and efficacy.

An Adaptive Self-Reduction Type-2 Fuzzy Clustering Algorithm for Pattern Recognition

Decisions in real-life can be adversely affected by various uncertainty-sources such as perception-diversity, data-structure and analytical tools. Fuzzy clustering can successfully handle the uncertainties while recognizing patterns in any given data. Nevertheless, type-1 fuzzy clustering techniques has uncertainties on account of precise-nature of primary memberships. Type-2 fuzzy clustering are preferred by many researchers to manage uncertainty in its type-1 version. In type-2 fuzzy clustering, order of fuzziness (fuzzifier) is obtained by interval-valued or general type-2 fuzzy sets. Interval type-2 fuzzy clustering can reduce the computational complexity of type-2 fuzzy set mathematics. However, general type-2 fuzzy clustering scrutinizes uncertainty in fuzzifier using linguistic sets. Interval and general type-2 fuzzy clustering algorithms include type-reduction approaches to obtain type-1 fuzzy sets. Besides, full type-2 fuzzy c-means can be used as a foundation approach in type-2 fuzzy inferences. Although this algorithm includes precise-fuzzifier, it gives a point of view to practically calculate secondary memberships. In this paper, an adaptive type-2 fuzzy clustering algorithm is proposed to manage the uncertainty-sources with a self-reduction procedure. Several numerical results and comparisons are given to demonstrate the achievement of this proposed algorithm. The performance of the proposed algorithm is compared with type-1 and type-2 versions for various multi-dimensional pattern sets from UCI-patterns, Berkeley segmentation database and a real-life application related to sustainable supplier selection in an automotive industry. Consequently, the proposed algorithm reveals fast, convenient and precise results.

A Novel Two-Stage Unsupervised Fault Recognition Framework Combining Feature Extraction and Fuzzy Clustering for Collaborative AIoT

Currently, with the development of the Internet of Things (IoTs) and artificial intelligence, a new IoT structure known as the artificial Intelligence of Things (AIoTs) comes into play. With the development of AIoT, a large amount of unlabeled industrial big data has been accumulated. The analysis of large amounts of unlabeled data is labor-intensive and time-consuming for diagnostic personnel. To improve this situation, a novel two-stage unsupervised fault recognition algorithm, namely, deep adaptive fuzzy clustering algorithm (DAFC) is proposed for unsupervised fault clustering in this article. DAFC amalgamates stacked sparse autoencoder (SSAE) into adaptive weighted Gath–Geva (AWGG) clustering to form an unsupervised fault recognition framework for clustering analysis of unlabeled industrial big data. SSAE can extract the highly abstract features of the original data, and adopt different unsupervised strategies to fine-tune the network in two stages. AWGG is an improvement of Gath–Geva clustering, and can adaptively obtain optimal clustering results without presetting the number of clusters. Experimental results on two different datasets show that the proposed DAFC can stably extract fault features from unlabeled data, and automatically obtain the optimal clustering results without knowing the number of clusters in advance. To the best of our knowledge, this article is the first attempt to fine-tune SSAE in an unsupervised manner, and to propose an unsupervised fault recognition framework that requires no prior knowledge or data labels at all. DAFC can be a feasible industrial big data application for collaborative AIoT. Diagnostic personnel analyze the clustering results obtained by DAFC instead of the original unlabeled data, greatly saving time and labor costs.

Adaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system

Adaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system

Soft Sensors Based on Adaptive Stacked Polymorphic Model for Silicon Content Prediction in Ironmaking Process

The silicon content of molten iron is a key index to reveal the thermal state in the blast furnace (BF). To improve the quality of molten iron, it is important to obtain silicon content online accurately. However, owing to the harsh environment in BF hearth, it is difficult to realize the online measurement of the silicon content of molten iron. To handle this problem, a novel soft sensoring method based on adaptive stacked polymorphic model is proposed. First, an adaptive uncertain fuzzy clustering algorithm (AUFC) is utilized to obtain the distribution characteristics and reduce the complexity of the process data, thereby the training samples are divided into several sub-classes. Second, the adaptive weight echo state network (AW-ESN) and time difference AW-ESN (TD-AWESN) are proposed to establish polymorphic models. Inspired by the stacking strategy, sub-models based on stacked polymorphic model are designed, which can effectively solve the time-varying and the strong nonlinear problems in the BF ironmaking process. Third, to avoid model degradation caused by the changes of working conditions, an ensemble strategy is adopted to combine the predicted results of multiple sub-models. The experimental results in an industrial BF show that the soft sensor based on an adaptive polymorphic model can reach the required prediction accuracy and provide reliable silicon content.

Suppressed robust picture fuzzy clustering for image segmentation

With the increase in sample number or image size, it is very important to improve the efficiency of picture fuzzy clustering algorithm in data classification or image segmentation. This paper presents an algorithm to improve the convergence speed of picture fuzzy clustering for large-scale data. According to the existing suppression fuzzy clustering idea, the maximum value of picture fuzzy partition information of picture fuzzy clustering is further increased and other values are decreased. The modified picture fuzzy partition information is used to update the cluster center of the sample, which speeds up the convergence speed of the algorithm. The adaptive selection of suppression factors makes the picture fuzzy clustering obtain faster operation efficiency and better segmentation performance. In order to further improve the performance of picture fuzzy clustering algorithm for noise image segmentation, an adaptive robust suppression picture fuzzy clustering algorithm with neighborhood spatial information constraints is proposed. Experimental results show that the proposed algorithm not only shortens the operation speed of existing picture fuzzy clustering, but also improves the robustness to noise.

An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar.

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

A Two-Stage Evolutionary Fuzzy Clustering Framework for Noisy Image Segmentation

This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization stage, the fuzzy clustering on pixels in inhomogeneous regions is converted into a multi-objective problem, which can preserve image details while restraining noise. The multi-objective problem is decomposed into several sub-problems by the Tchebycheff approach. A trade-off can be obtained by optimizing these sub-problems simultaneously. In the fuzzy clustering segmentation stage, fuzzy clustering with the trade-off between preserving image details and restraining noise is performed on the whole observed image. To deal with this fuzzy clustering problem, an adaptive evolutionary fuzzy clustering algorithm with spatial information is proposed. Experiment results on synthetic and real images illustrate the effectiveness of the proposed framework for noisy image segmentation.

Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing

Energy is a precious resource in the sensors-enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly, which may interrupt the operations in the network. Further, a single artificial intelligence technique is not enough to solve the problem of load balancing and minimize energy consumption, because of the integration of ubiquitous smart-sensors-enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesized fuzzy logic and a neural network to counterbalance the selection of the optimal number of cluster heads and even distribution of load among the sensors. We developed fuzzy rules, sets, and membership functions of an adaptive neuro fuzzy inference system to decide whether a sensor can play the role of a cluster head based on the parameters of residual energy, node distance to the base station, and node density. The proposed ANFCA outperformed the state-of-the-art algorithms in terms of node death rate percentage, number of remaining functioning nodes, average energy consumption, and standard deviation of residual energy.

Method of Estimating the Geometric Parameters of a Three-Dimensional Object from Resistivity Survey Data

The testing of the proposed method for estimating the geometric parameters of a three-dimensional object from the data of computer-aided simulation and field experiments confirmed by excavation provide the possibility to determine the location of anomalous-resistance objects under the ground and to perform the quantitative estimation of their geometric parameters (shape, dimensions, and burial depth) from resistivity survey data. It is shown that the analysis of the vector pictures of main resistance change directions provides the possibility to estimate the spatial location of an object of search (along the depth and in horizontal “sections”) at a qualitative level and to determine its relative resistance. The application of the scalar product function and the adaptive fuzzy clustering algorithm to these vector pictures provides the possibility to estimate the shape of an object of search and the range of its burial depths. Using the A* optimal path search algorithm, it is possible to plot the boundary line of an object on a set of horizontal “sections.”

Recognition of High-Voltage Cable Partial Discharge Signal Based on Adaptive Fuzzy C-Means Clustering

Partial discharge (PD) detection is an effective means to find high-voltage cable defects. However, various interference signals affect the PD signal in practical applications, resulting in wrong judgment. In order to improve the accuracy of PD detection of high voltage cables, an adaptive fuzzy C-means (FCM) clustering was proposed to identify PD signals. The adaptive threshold pulse extraction algorithm based on fixed interval width was employed. The threshold was changed adaptively to extract the effective PD pulse waveform according to the change of the background noise and the degree of PD. Then PD pulse features were analyzed in time and frequency domain by employing the equivalent time frequency analysis method. The adaptive fuzzy clustering algorithm was used to classify the signals. The phase distribution concentration of all kinds of pulse signal was calculated. The phase standard deviation of various types of pulse was taken as the index that measures the concentration of density to distinguish between PD and interference signals. Results show that the adaptive FCM clustering algorithm, compared with the traditional method, can not only identify the PD signal accurately, but also be conscious of the PD category. The PD recognition method proposed in this paper has strong applicability and high accuracy, which is particularly suitable for application in the field of engineering.

Infrared image segmentation based on gray-scale adaptive fuzzy clustering algorithm

Since the infrared detector itself is subject to various external disturbances when collecting information, infrared images are characterized by of low SNR, low contrast and blur edge, which greatly increases the difficulty of detection and recognition. Contraposing the problems that a fuzzy clustering algorithm cannot find the reasonable clustering number adaptively, it will have a low infrared image segmentation rate when the gray-scale between object region and background region are of great difference. A gray-scale adaptive fuzzy clustering algorithm (GAFC) is proposed in this work. The methodology uses a coarse-fine concept to reduce the computational burden required for the fuzzy clustering and to improve the accuracy of segmentation that a single fuzzy clustering cannot reach. The coarse segmentation attempts to segment coarsely based on gray level histogram. Firstly, the pseudo peaks in the gray level histogram are removed by introducing a control factor of peak areas and a control factor of peak widths, then in order to find a finer segmentation result, the coarse segmentation result is clustered by an improved fuzzy clustering algorithm that introduces an adaptive function to get the most reasonable cluster number and that defines a logarithmic function as a measurement of distance. The results of experimental data show that not only the GAFC mentioned in this paper preserves the advantages in multi-threshold segmentation method which is fast and easy, and behaves well in segmenting infrared images in complex environments.

Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote Sensing Imagery

Due to the intrinsic complexity of remote sensing images and the lack of prior knowledge, clustering for remote sensing images has always been one of the most challenging tasks in remote sensing image processing. Recently, clustering methods for remote sensing images have often been transformed into multiobjective optimization problems, making them more suitable for complex remote sensing image clustering. However, the performance of the multiobjective clustering methods is often influenced by their optimization capability. To resolve this problem, this paper proposes an adaptive multiobjective memetic fuzzy clustering algorithm (AFCMOMA) for remote sensing imagery. In AFCMOMA, a multiobjective memetic clustering framework is devised to optimize the two objective functions, i.e., $Jm$ and the Xie-Beni $(XB) $ index. One challenging task for memetic algorithms is how to balance the local and global search capabilities. In AFCMOMA, an adaptive strategy is used, which can adaptively achieve a balance between them, based on the statistical characteristic of the objective function values. In addition, in the multiobjective memetic framework, in order to acquire more individuals with high quality, a new population update strategy is devised, in which the updated population is composed of individuals generated in both the local and global searches. Finally, to evaluate the proposed AFCMOMA algorithm, experiments using three remote sensing images were conducted, which confirmed the effectiveness of the proposed algorithm.

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information for Remote Sensing Imagery

Due to its inherent complexity, remote sensing image clustering is a challenging task. Recently, some spatial-based clustering approaches have been proposed; however, one crucial factor with regard to their clustering quality is that there is usually one parameter that controls their spatial information weight, which is difficult to determine. Meanwhile, the traditional optimization methods of the objective functions for these clustering approaches often cannot function well because they cannot simultaneously possess both a local search capability and a global search capability. Furthermore, these methods only use a single optimization method rather than hybridizing and combining the existing algorithmic structures. In this paper, an adaptive fuzzy clustering algorithm with spatial information for remote sensing imagery (AFCM_S1) is proposed, which defines a new objective function with an adaptive spatial information weight by using the concept of entropy. In order to further enhance the capability of the optimization, an adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery (AMASFC) is also proposed. In AMASFC, the clustering problem is transformed into an optimization problem. A memetic algorithm is then utilized to optimize the proposed objective function, combining the global search ability of a differential evolution algorithm with a local search method using Gaussian local search (GLS). The optimal value of the specific parameter in GLS, which determines the local search efficiency, can be obtained by comparing the objective function increment for different values of the parameter. The experimental results using three remote sensing images show that the two proposed algorithms are effective when compared with the traditional clustering algorithms.

An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation

This paper presents an adaptive spatial information-theoretic fuzzy clustering algorithm to improve the robustness of the conventional fuzzy c-means (FCM) clustering algorithms for image segmentation. This is achieved through the incorporation of information-theoretic framework into the FCM-type algorithms. By combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the problems of sensitivity to noisy data and the lack of spatial information, and improve the image segmentation results. The experimental results have shown that this robust clustering algorithm is useful for MRI brain image segmentation and it yields better segmentation results when compared to the conventional FCM approach.

Adaptive Particle Swarm-Based Fuzzy Clustering Algorithm in the Application of Steam Drum Pulverized Coal Fired Boiler

The Pulverized Coal Fired Boiler is a control object with time-varying parameters, large delay and multivariate tight-coupling. This paper, aiming at improving the actualities of excessive parameters and suboptimal data clustering effect in the Steam Drum Pulverized Coal Boiler running, proposes a new algorithm Fuzzy Clustering Algorithm (APFM) to fulfill the efficiency optimization of the boiler combustion. Based on the Adaptive Particle Swarm Optimization Algorithm (APSO), this new algorithm overcomes the disadvantages of FCM algorithm such as the sensitivity to initial value and the easy involvement in partial optimum, and enhances the abilities of APSO algorithm such as global search and escape from partial optimum. Finally, the feasibility and effectiveness of this clustering method is demonstrated through the simulation experiment.

A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval

In this paper an adaptive hierarchical fuzzy clustering algorithm is presented, named Hierarchical Data Divisive Soft Clustering (H2D-SC). The main novelty of the proposed algorithm is that it is a quality driven algorithm, since it dynamically evaluates a multi-dimensional quality measure of the clusters to drive the generation of the soft hierarchy. Specifically, it generates a hierarchy in which each node is split into a variable number of sub-nodes, determined by an innovative quality assessment of soft clusters, based on the evaluation of multiple dimensions such as the cluster’s cohesion, its cardinality, its mass, and its fuzziness, as well as the partition’s entropy. Clusters at the same hierarchical level share a minimum quality value: clusters in the lower levels of the hierarchy have a higher quality; this way more specific clusters (lower level clusters) have a higher quality than more general clusters (upper level clusters). Further, since the algorithm generates a soft partition, a document can belong to several sub-clusters with distinct membership degrees. The proposed algorithm is divisive, and it is based on a combination of a modified bisecting K-Means algorithm with a flat soft clustering algorithm used to partition each node. The paper describes the algorithm and its evaluation on two standard collections.

Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means

A neural network soft sensor based on fuzzy clustering is presented. The training data set is separated into several clusters with different centers, the number of fuzzy cluster is decided automatically, and the clustering centers are modified using an adaptive fuzzy clustering algorithm in the online stage. The proposed approach has been applied to the slab temperature estimation in a practical walking beam reheating furnace. Simulation results show that the approach is effective.

A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image

A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The segmentation target therefore is driven by two forces to smooth the derived optimal bias field and improve the accuracy of the segmentation task. The proposed method has been successfully applied to 3 T, 7 T, synthetic and real MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. Moreover, the proposed algorithm is robust to initialization, thereby allowing fully automatic applications.