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A novel model of fuzzy clustering neural network is discussed, which synthesizes unsupervised fuzzy competitive learning algorithm and self-organized competitive network. Based on this model, an algorithm of abrupt video shot boundary detection is presented which is a two-stage clustering on a linear feature space. The experimental results obtained demonstrate that the algorithm is feasible and efficient.
In this paper we investigate the possibility of using ANN for modeling uncertainty of models (with some focus on environmental models). We assume that all uncertainties of the prediction made by such model M are represented by probability distribution function (pdf) of its error, and build regression models of the quantiles of this pdf. The original version of the technique termed UNEEC (published...
In order to overcome inherent bugs of basic hidden markov model (HMM), a method of speech recognition based on fuzzy clustering neural network is presented. Based on the fuzzy system model, every state (HMM) is regarded as a fuzzy system in this method. With continuous frames character vector of speech signal as the system's input, the model can forecast the probability density function of the system's...
A method to partition the universe of discourse based on fuzzy clustering is proposed to solve the partition problem in the process of constructing rough neural network. Considering traditional clustering algorithm has the problem of easily fall into local optimum, a modified PSO algorithm with crossover and mutation operators is combined with FCM algorithm. And a new fuzzy clustering algorithm (CMPSO-FCM)...
Due to the fact that the detection of intrusion is inefficient and lacks intelligence in current intrusion detection system, this paper integrates BP neural network and support vector machine (SVM) based on the theory of neural network integration, applying fuzzy clustering technology to cluster data, choosing data from the cluster centre to train ensemble individuals, then selecting and integrating...
The classification and identification technology plays an important role in the research of brain-computer interface (BCI) systems. In this paper, we do fuzzy clustering disposal for the multi-channel electroencephalogram (EEG) during finger movement at first according to event-related desynchronization phenomena (ERD) in the event-related EEG. Then we classify signal-trial EEG with the feature extracted...
This article deals with the development of an improved clustering technique for categorical data that is based on the identification of points having significant membership to multiple classes. Cluster assignments of such points are difficult, and they often affect the actual partitioning of the data. As a consequence, it may be more effective if the points that are associated with maximum confusion...
In this paper two fuzzy clustering algorithms, namely fuzzy C-means (FCM) and Gustafson Kessel clustering (GKC), have been used for detecting changes in multitemporal remote sensing images. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. To show the effectiveness of the proposed technique, experiments are conducted on three multispectral...
Architecture of self-learning fuzzy spiking neural network that belongs to a new type of hybrid intelligence systems is proposed. Both crisp and fuzzy clustering modes are described. Results of image processing in the presence of overlapping classes are presented.
The efficiency frontier analysis has been an important approach of evaluating firms' performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes a nonparametric efficiency...
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