Fuzzy Min-max Clustering Research Articles

A modified fuzzy min–max neural network for data clustering and its application on pipeline internal inspection data

In this paper, a modified fuzzy min–max neural network (MFMC) for data clustering is proposed. In MFMC, the centroid information, the similarity and the noise of data are taken into the consideration. What’s more, the hyperbox entropy (HE) is first introduced to evaluate the performance of each hyperbox when doing the contraction process. In addition, in order to test the performance of the MFMC model, a series of simulations on benchmark data sets are conducted. Then a real-world application study on the pipeline internal inspection data is also performed. The experimental result indicates that the MFMC has more excellent performance than other existed fuzzy min–max clustering algorithms.

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.

Estuary water-stage forecasting by using radial basis function neural network

The Radial basis function neural network (RBFNN) has been successfully applied to many tasks due to its powerful properties in classification and functional approximation. This paper presents a novel RBFNN for water-stage forecasting in an estuary under high flood and tidal effects. The RBFNN adopts a hybrid two-stage learning scheme, unsupervised and supervised learning. In the first scheme, fuzzy min–max clustering is proposed for choosing best patterns for cluster representation in an efficient and automatic way. The second scheme uses supervised learning, which is a multivariate linear regression method to produce a weighted sum of the output from the hidden layer. Since this network has only one layer using a supervised learning algorithm, its training process is much faster than the error back propagation based multilayer perceptrons. Moreover, only one parameter, θ, must be determined manually. The other parameters used in this model can be adjusted automatically by model training. The water-stage data of the Tanshui River under tidal effect are used to construct a water-stage forecasting model that can also be used during flood. The results show that the RBFNN can be applied successfully and provide high accuracy and reliability of water-stage forecasting in an estuary.

Fuzzy Clustering Neural Network as Flood Forecasting Model

Flood forecasting is always a challenge in Taiwan, which has a subtropical climate and high mountains. This paper develops a fuzzy clustering neural network (FCNN), and implements this novel structure and reasoning process for flood forecasting. The FCNN has a hybrid learning scheme; the unsupervised learning scheme employs fuzzy min-max clustering to extract information from the input data. The supervised learning scheme uses linear regression to determine the weights of FCNN. The network, which learns from examples, is a hydrological processes theory-free estimator. Most of the parameters, weights of the network, are adjusted automatically during the network training. Only one parameter needs to be justified during constructing the flood forecasting models. The one-hour-ahead floods of the Lanyoung River during tropical storms are forecasted by the constructed models. Our results show that the simple but reliable model is capable of real time flood forecasting.

Fuzzy hyperline segment clustering neural network

A fuzzy hyperline segment clustering neural network (FHLSCNN) and its learning algorithm is proposed. This algorithm can learn ill-defined nonlinear cluster boundaries in a few passes and is suitable for on-line adaptation. The FHLSCNN is superior compared to the fuzzy min-max clustering neural network (FMN) proposed by Simpson.

Flood forecasting using radial basis function neural networks

A radial basis function (RBF) neural network (NN) is proposed to develop a rainfall-runoff model for three-hour-ahead flood forecasting. For faster training speed, the RBF NN employs a hybrid two-stage learning scheme. During the first stage, unsupervised learning, fuzzy min-max clustering is introduced to determine the characteristics of the nonlinear RBFs. In the second stage, supervised learning, multivariate linear regression is used to determine the weights between the hidden and output layers. The rainfall-runoff relation can be considered as a linear combination of some nonlinear RBFs. Rainfall and runoff events of the Lanyoung River collected during typhoons are used to train, validate,and test the network. The results show that the RBF NN can be considered a suitable technique for predicting flood flow.

General fuzzy min-max neural network for clustering and classification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms developed by Simpson. The GFMM method combines the supervised and unsupervised learning within a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. This hybrid system exhibits an interesting property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes through the data and consists of placing and adjusting the hyperboxes in the pattern space which is referred to as an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given.

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.

Fuzzy min-max neural networks - Part 2: Clustering

In an earlier companion paper (56) a supervised learning neural network pattern classifier called the fuzzy min- max classification neural network was described. In this sequel, the unsupervised learning pattern clustering sibling called the fuzzy min-max clustering neural network is presented. Pattern clusters are implemented here as fuzzy sets using a membership function with a hyperbox core that is constructed from a min point and a max point. The min-max points are determined using the fuzzy min-max learning algorithm, an expansion-contraction process that refines the author's earlier Fuzzy Adaptive Reso- nance Theory neural network (50). The fuzzy min-max clustering neural network stabilizes into pattern clusters in only a few passes through a data set; it can be reduced to hard cluster boundaries that are easily examined without sacrificing the fuzzy boundaries; it provides the ability to incorporate new data and add new clusters without retraining; and it inherently provides degree of membership information that is extremely useful in higher level decision making and information processing. This paper will provide some background concerning the development of the fuzzy min-max clustering neural network and provide a comparison with similar work that has recently emerged. A brief description of fuzzy sets, pattern clustering, and their synergistic combination is presented. The fuzzy min- max clustering neural network will be explained in detail and examples of its clustering performance will be given. The paper will conclude with a description of problems that need to be addressed and a list of some potential applications.