Adaptive Fuzzy Clustering Research Articles

Segment-Based Map Building Using Enhanced Adaptive Fuzzy Clustering Algorithm for Mobile Robot Applications

In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.

An Adaptive Fuzzy Clustering Technique for Traffic Prediction of Packet-switched Networks

Traffic prediction is significant to QoS design because it assists efficient management of network resources to improve the reliability and performance of the next generation Internet. The unavoidable traffic variation caused by diverse Internet services complicates traffic prediction, particularly in a multi-hop network. To simplify the complicated statistical analysis used in traditional approaches, an adaptive traffic prediction approach featuring robustness, high accuracy and high adaptability is proposed in this paper. The proposed approach bases on a novel fuzzy clustering algorithm to generalize and unveil the hidden structure of traffic patterns. The unveiled structure represents the characteristics of the target traffic. Therefore, it can be referenced to predict traffic in a limited time period by fuzzy matching. To track the variation of target traffic, the proposed approach adopts an incremental and dynamic on-line clustering procedure so that the prediction can maintain high accuracy under traffic variation. To verify the performance of the proposed approach and investigate its properties, the periodical, Poisson and real video traffic patterns have been used to experiment. The experimental results showed an excellent performance of the developed adaptive predictor. The prediction errors, in average, are near 2.2%, 13.6% and 7.62% for periodical, Poisson and real video traffics, respectively.

Multiple neural networks-integrated underwater target classification based on fuzzy theory

In the area of underwater target recognition, in order to process various target feature data extracted from underwater radiating noise signals, a target classification method based on the integration of multiple fuzzy clustering neural networks and fuzzy comprehensive analysis is put forward in this paper. An adaptive fuzzy clustering neural network classifier is constructed and a relative studying algorithm is described for the first class classification, which is applied separately to different kinds of feature data from LOFAR spectrum, bispectrum, and wavelet transformation. Based on fuzzy comprehensive analysis of the outputs and probabilities of the above neural network classifiers and the category and distribution of test samples, the second class fuzzy classifying rules are designed to preprocess and assess the categories of ships. More than 4000 passive sonar signal samples are collected and many tests are done, and the results show the feasibility, adaptability, and effectiveness of the present method.

Supervised fuzzy clustering for rule extraction

This paper deals with the application of orthogonal transforms and fuzzy clustering to extract fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with respect to describing the data. Clustering takes place in the product space of systems inputs and outputs and each cluster corresponds to a fuzzy IF-THEN rule. By initializing the clustering with an overestimated number of clusters and subsequently remove less important ones as the clustering progresses, it is sought to obtain a suitable partition of the data in an automated manner. The approach is generally applicable to the fuzzy c-means and related algorithms. The adaptive distance norm fuzzy clustering is studied and applied to the identification of Takagi-Sugeno type rules. Both a synthetic example as well as a real-world modeling problem are considered to illustrate the working and the applicability of the algorithm.

A FUZZY ROUTE GUIDANCE MODEL FOR INTELLIGENT IN-VEHICLE NAVIGATION SYSTEMS

This paper presents an adaptive fuzzy clustering model that can be used to identify nature subgroups of links as well as priority memberships in a route guidance system. The fuzzy route guidance model, inspired by the fuzzy clustering technique, provides an adaptive and efficient alternative to traditional fixed costs route guidance methods. Three specific objectives underlie the presentation of the fuzzy route guidance model in this paper. The first is to describe a general overview of the in-vehicle navigation system, and the second is to introduce the fuzzy route guidance model based on adaptive fuzzy clustering and least cost problem. The third part is to demonstrate that the proposed model is able to perform route guidance in road test.

A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms

In this letter, we show that the Gustafson-Kessel (G-K) algorithm (1979) and the original adaptive fuzzy clustering (AFC) algorithm can be thought of as special cases of a more general algorithm. Our analysis shows that the G-K algorithm is better suited for ellipsoidal clusters of equal volume, whereas the original AFC algorithm is better suited for linear clusters. We also discuss a new variation of these algorithms, which can be used to improve the results of the G-K and AFC algorithms in some cases.

High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images

An adaptive vector quantizer (VQ) using a clustering technique known as adaptive fuzzy leader clustering (AFLC) that is similar in concept to deterministic annealing (DA) for VQ codebook design has been developed. This vector quantizer, AFLC-VQ, has been designed to vector quantize wavelet decomposed sub images with optimal bit allocation. The high-resolution sub images at each level have been statistically analyzed to conform to generalized Gaussian probability distributions by selecting the optimal number of filter taps. The adaptive characteristics of AFLC-VQ result from AFLC, an algorithm that uses self-organizing neural networks with fuzzy membership values of the input samples for upgrading the cluster centroids based on well known optimization criteria. By gen- erating codebooks containing codewords of varying bits, AFLC-VQ is capable of compressing large color/monochrome medical images at extremely low bit rates (0.1 bpp and less) and yet yielding high fidelity reconstructed images. The quality of the reconstructed im- ages formed by AFLC-VQ has been compared with JPEG and EZW, the standard and the well known wavelet based compression tech- nique (using scalar quantization), respectively, in terms of statistical performance criteria as well as visual perception. AFLC-VQ exhibits much better performance than the above techniques. JPEG and EZW were chosen as comparative benchmarks since these have been used in radiographic image compression. The superior perfor- mance of AFLC-VQ over LBG-VQ has been reported in earlier pa- pers. © 1999 SPIE and IS&T. (S1017-9909(99)01301-X)

Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions

We present an adaptive fuzzy clustering scheme for image segmentation, the adaptive fuzzy clustering/segmentation (AFCS) algorithm. In AFCS, the nonstationary nature of images is taken into account by modifying the prototype vectors as functions of the sample location in the image. The inherent high interpixel correlation is modeled using neighborhood information. A multiresolution model is utilized for estimating the spatially varying prototype vectors for different window sizes. The fuzzy segmentations at different resolutions are combined using a data fusion process in order to compute the final fuzzy partition matrix. The results provide segmentations, having lower fuzzy entropy when compared to the possibilistic C-means algorithm, while maintaining the image's main characteristics. In addition, due to the neighborhood model, the effects of noise in the form of single pixel regions are minimized.

A comparison of self-organizing neural networks for fast clustering of radar pulses

Four self-organizing neural networks are compared for automatic deinterleaving of radar pulse streams in electronic warfare systems. The neural networks are the Fuzzy Adaptive Resonance Theory, Fuzzy Min–Max Clustering, Integrated Adaptive Fuzzy Clustering, and Self-Organizing Feature Mapping. Given the need for a clustering procedure that offers both accurate results and computational efficiency, these four networks are examined from three perspectives – clustering quality, convergence time, and computational complexity. The clustering quality and convergence time are measured via computer simulation, using a set of radar pulses collected in the field. Estimation of the worst-case running time for each network allows for the assessment of computational complexity. The effect of the pattern presentation order is analyzed by presenting the data not just in random order, but also in radar-like orders called burst and interleaved.

Adaptation and Learning with Fuzzy Clustering Algorithms: A Data Re-Initialization (DR) Scheme

Abstract This paper presents a novel idea to avoid large transient error of hill-climbing type adaptive algorithms in abrupt changing environments. A data re-initialization (DR) scheme is proposed using multiple models approach with special initialization technique for smooth transition between different environments. The design is based on adaptive fuzzy clustering algorithm with on-line modifications; furthermore, it is equipped with learning capabilities. The fast adaptation is realized by DR and switching between different models; learning is realized by recording and retrieving the trained up models. The algorithm is conceptually simple and simulation results are appealing. Finally, conclusion and remarks are given.

適応型ファジィクラスタリングによる分類と回帰の同時分析

適応型ファジィクラスタリングによる分類と回帰の同時分析

Look-up table fuzzy clustering algorithm for codebookdesign in vector quantisation

The authors propose a look-up table fuzzy clustering algorithm using a membership function with adaptive fuzziness. The use of the look-up-table method avoids bulky exponential data calculation in the FCM algorithm. The use of the adaptive fuzzy clustering algorithm also largely improves the codebook quality in image processing by reducing the mean square error (MSE) of 26.18% and 8.67% shown in recent LBG and FCM algorithms, respectively.

An adaptive integrated fuzzy clustering model for pattern recognition

Abstract The extension of neural net based crisp clustering algorithms to fuzzy clustering algorithms has been addressed by many researchers in recent years. However, such neuro-fuzzy clustering algorithms developed so far suffer from restrictions in identifying the actual decision boundaries among clusters with overlapping regions. These restrictions are induced by the choice of the similarity measure and representation of the clusters. An integrated adaptive fuzzy clustering (IAFC) algorithm is presented to generate improved decision boundaries by introducing a new similarity measure and by integrating the advantages of the fuzzy optimization constraint of fuzzy c-means (FCM), the control structure of adaptive resonance theory (ART-1), and a fuzzified Kohonen-type learning rule. The effect of the new similarity measure in finding nonlinear decision boundaries among closely located cluster centroids is demonstrated with computer generated data. We use the IRIS data set and a subset of the tethered satellite system simulation data set to compare the convergence rate and misclassifications resulting from IAFC algorithm with other clustering algorithms.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid position from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The AFLC algorithm is applied to the Anderson iris data and laser-luminescent finger image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets.

Adaptive fuzzy c-shells clustering and detection of ellipses

Several generalizations of the fuzzy c-shells (FCS) algorithm are presented for characterizing and detecting clusters that are hyperellipsoidal shells. An earlier generalization, the adaptive fuzzy c-shells (AFCS) algorithm, is examined in detail and is found to have global convergence problems when the shapes to be detected are partial. New formulations are considered wherein the norm inducing matrix in the distance metric is unconstrained in contrast to the AFCS algorithm. The resulting algorithm, called the AFCS-U algorithm, performs better for partial shapes. Another formulation based on the second-order quadrics equation is considered. These algorithms can detect ellipses and circles in 2D data. They are compared with the Hough transform (HT)-based methods for ellipse detection. Existing HT-based methods for ellipse detection are evaluated, and a multistage method incorporating the good features of all the methods is used for comparison. Numerical examples of real image data show that the AFCS algorithm requires less memory than the HT-based methods, and it is at least an order of magnitude faster than the HT approach.

Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering

A new approach to visual evaluation of long-term EEG recordings is proposed. The method is based on multichannel adaptive segmentation, subsequent feature extraction, automatic classification of the acquired segments by fuzzy cluster analysis (fuzzy c-means algorithm), and on the distinguishing of thus identified EEG segments by colour directly in the EEG record. The black and white variant of the described automatic system is presented. The method was evaluated by applying it to simulated artificial data and to real EEG recordings: some of the illustrative results are shown. In addition, the performance of this system is evaluated and the first experience with its application to routine EEG recordings is discussed.