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Out of several antenna design techniques the Artificial Neural Network (ANN) based method is suitable for prediction of characteristic parameters of loop antenna by considering transmit - receive conditions of practical communication set-ups. The predicted set of parameters can be used to fix dimensions of a loop antenna which involves theoretical calculations. This work proposes an approach to determine...
This paper presents method used hand gesture recognition in human-computer interaction and control. Nowadays in dataglove-driven motion capture field, researchers preprocess the raw sensor data of the glove with calibration methods for acquiring a high precision in the VR environment. But there are still alternative solutions. Some machine learning algorithms, for example the self-organizing map method,...
Most architecture of mobile ad hoc network is in the form of decentralized, self-configuring and dynamic topologies. Nodes are mobile in network. The mobility of node in network is common problem in peer-to-peer technology. Object replication is one of the techniques applied in order to share objects in the mobile peer-to-peer environment. Predicting the estimated time for the node to exit is a great...
Classification is a major problem of study that involves formulation of decision boundaries based on the training data samples. The limitations of the single neural network approaches motivate the use of multiple neural networks for solving the problem in the form of ensembles and modular neural networks. While the ensembles solve the problem redundantly, the modular neural networks divide the computation...
Kohonen's self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. In general, the SOM neural network is constructed as a learning algorithm for numerical data. However, except these numeric data, there are many other data types such as symbolic data. Thus, Yang et al. proposed...
In order to resolve the computational complexity for local map matching of hierarchical simultaneous localization and mapping (SLAM), a novel self-organizing fuzzy neural networks (SOFNN) based approach was proposed in this paper. The matching component for local maps in the hierarchical SLAM is realized by an SOFNN. A subset of signature elements included in a local map was chosen by a clustering...
The paper describes an unsupervised model for classifying Web service datatypes into a large number of classes specified by an ontology. As a result of the classification, each datatype component of a Web service is associated to one ontology concept, the name of which is further used to semantically annotate the datatype. The framework is based on an extended model of hierarchical self-organizing...
An incremental intrusion detecting model is proposed in this paper. This model integrates unsupervised Self Organizing Map and supervised Radial Basis Function to complete incremental learning. Self Organizing Map can get new type intrusion information and generate new nodes in Radial Basis Function. By this model, intrusion of unknown type can be detected online. Experiment results show our model...
In this work the self-organizing fuzzy neural network (SOFNN) is employed to create an accurate and easily calibrated approach to multiple-step-ahead prediction for the NN5 forecasting competition 2008. The competition dataset consists of 111 daily empirical time series of cash-machine withdrawals. The objective for the competition was to forecast future transactions up to 56 days ahead with the highest...
Self-organising neural networks have shown promise in a variety of applications areas. Their massive and intrinsic parallelism makes those networks suitable to solve hard problems in image-analysis and computer vision applications, especially when non-stationary environments occur. Moreover, this kind of neural networks preserves the topology of an input space by using their inherited competitive...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
In this paper, a Self-organizing Fuzzy Neural Network employing an Extended Kalman Filter (EKF), termed Self-organizing Fuzzy Neural Networks with Extended Kalman Filter (SOFNNEKF) is designed and developed. The learning algorithm based on an EKF is simple and effective and is able to generate a fuzzy neural network with a high accuracy and compact structure. The structure learning of the SOFNNEKF,...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
For a developmental robotic system to function successfully in the real world, it is important that it be able to form its own internal representations of affordance classes based on observable regularities in sensory data. Usually successful classifiers are built using labeled training data, but it is not always realistic to assume that labels are available in a developmental robotics setting. There...
Gear mechanisms are an important element in a variety of industrial applications. An unexpected failure of the gear mechanism may cause significant economic losses. Efficient incipient faults detection and accurate faults diagnosis are therefore critical to machinery normal running. In this paper a novel method is presents to enhance the detection and diagnosis of gear multi-faults based on Autoregressive...
Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult. We present an unsupervised neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map neural network. In this process, the images for the different persons will be scanned and it will be...
This paper presents realization and the laboratory tests of the Kohonen winner takes all (WTA) neural network (NN) realized on microcontrollers (μC) with the AVR and ARM CortexM3 cores. Both μCs have been placed on a single testing board especially designed for this purpose. The board also contains an interface block with an analog-to-digital and digital-to-analog converters (ADC/DAC). The learning...
Video artificial text detection is a challenging problem of pattern recognition. Current methods which are usually based on edge, texture, connected domain, feature or learning are always limited by size, location, language of artificial text in video. To solve the problems mentioned above, this paper applied SOM (Self-Organizing Map) based on supervised learning to video artificial text detection...
It is fundamental work to translate the historical characters called "kuzushi-ji" into the contemporary characters in Japanese historical studies. In this paper, we develop the Japanese historical character recognition system using the directional element features and modular neural networks. Modular neural networks consist of two kinds of classifiers: a rough classifier to find the several...
A reliable and accurate short-term traffic forecasting system is crucial for the successful deployment of any intelligent transportation system. To address the complexity of real-world traffic forecasting conditions, this paper presents a layered traffic forecasting algorithm, which is implemented by a clustering neural network, Kohonen self-organizing map (KSOM) and four neural network paradigms...
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