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This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques The object recognition is accomplished using a neuronal network with FuzzyARTMAP architecture...
In this paper, a supervised learning strategy based on a Multi-Objective Particle Swarm Optimization (MOPSO) is introduced for ARTMAP neural networks. It is based on the concept of neural network evolution in that particles of a MOPSO swarm (i.e., network solutions) seek to determine user-defined parameters and network (weights and architecture) such that generalisation error and network resources...
Land-use change is an important area of global change research, rapid and accurate access to land-use temporal and spatial variation information is a key technology to study land-use change. In this paper proposed a method which utilizes the improved model of fuzzy ARTMAP network - simplified fuzzy ARTMAP neural network for remote sensing land-use classification, and Take the TM remote sensing image...
This paper presents a neural network classifier based on fuzzy ARTMAP with conflict-resolving strategy. The proposed model explicitly resolves overlaps among prototypes of different classes through deploying a contraction procedure in the network, therefore, improving its generalization. Compared with other existing methods, the model has the priority of intuition and no parameter tuning. The performance...
A correct video segmentation, namely the detection of moving objects within a scene plays a very important role in many application in safety, surveillance, traffic monitoring and object detection. The main objective of this paper is to implement an effective background segmentation algorithm for corner sets extracted from video sequences. A dynamic prototype of the structure of background corners...
An evolutionary approach has been proposed to improve simplified fuzzy ARTMAP neural network performance for off-line font-based recognition of printed Persian alphabetical characters. Some of Persian characters are so similar to each other. We have defined and used some fuzzy sets in feature extraction to improve recognition of these characters. Also, the presentation order of training patterns to...
Wireless sensor networks are generally deployed in remote areas where no infrastructure is available. This imposes the use of battery operated devices which seriously limits the lifetime of the network. In this paper we present a cluster-based routing algorithm which is based on Fuzzy-ART neural networks to maximize the life span of such networks. Results show that the energy saving obtained improves...
Working conditions are monitoring parameters are huge and neural network learning time too long in the condition monitoring of multi word condition equipment. To improve monitoring efficiency, a joint rough set attribute reduction (RSAR) and Fuzzy ART (adaptive resonance theory) neural network method is proposed in this study. The dimension of an input vector to Fuzzy ART neural networks can be reduced...
We have developed a novel method to estimate missing observations in wireless sensor networks. We use a hierarchical unsupervised fuzzy ART neural network to represent the data cluster prototypes. We then estimate missing inputs by using a new spatial-temporal imputation technique. We have evaluated this approach through experiments on both real sensor data and artificially generated data. Our experimental...
After the modification of the fuzzy ART neural network operations. An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time is designed and realized. The simulation experiment results show an online maximum recognition rate is 86.3% and offline recognition rates are nearly 100% when the proper network parameters...
This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness...
Early detection of a tumorpsilas site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. Here, we apply a neural network clustering theory, Fuzzy ART, to generate the division of cancer samples, which is useful...
This paper presents an investigation of the influence of the RePART (Reward and Punishment ARTmap) neural network in structures of ensembles designed by three variants of boosting: Aggressive, Conservative and Inverse Boosting. In this investigation, it is aimed to analyze whether the use of this model is positive for ARTMAP-based ensembles. In addition, it aims to define which boosting strategy is...
Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrain from the start using all the cumulative training data. In this paper, the performance of two such classifiers - the fuzzy ARTMAP and Gaussian ARTMAP neural networks - are characterize and compared for supervised incremental learning...
We present application of data mining, and in particular, fuzzy ARTMAP neural networks, in classification of peer-to-peer (P2P) traffic in IP networks. We captured Internet traffic at a main gateway router, performed pre-processing on the data, selected the most significant attributes, and prepared a training data set to which the fuzzy ARTMAP algorithms were applied. Fuzzy ARTMAP is an incremental...
In this study, we applied biorthogonal wavelets to extract essential features of the ballistocardiogram (BCG) signal and to classify them using a novel neural network so-called supervised fuzzy adaptive resonance theory (SF-ART). SF-ART has two stages. At first stage, pre-classification level, the input data is clustered roughly to arbitrary (M) classes using self-organized fuzzy ART tuned for fast...
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