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This paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a powerful scheme for learning complex input-output mapping have been used in learning of the textual descriptors in a paragraphs of an author. The resulting training information we get will be used to identify...
Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural Network (ANN) based cardiac arrhythmia...
This paper presents the first results of a research project aimed at building a pollution peaks predictor using Artificial Neural Networks (ANNs) with data measured locally. We focus more particularly on the ozone concentration prediction in the Corsica Island at horizon “h+1”. We mainly look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures both in the Environment...
The launch of last-generation satellites (COSMO-SkyMed and TerraSAR-X), equipped with X-band sensors acquiring images with a very high spatial resolution, has opened up new challenges in the field of SAR image processing for remote sensing applications. In this work, a set of Spotlight and Stripmap COSMO-Skymed images taken the Tor Vergata-Frascati test site was considered to investigate on the potential...
Impervious surface plays an important role in monitoring urbanization and related environmental changes. CBERS and HJ-1 satellite images were employed to impervious surface extraction. Xuzhou City, located in the northwestern of Jiangsu Province, China, was chosen as the case study area. Using linear spectral mixture model (LSMM) and multi-layer perception (MLP) neural network, all pixels were decomposed...
A prediction scheme for sunspot series using a Recurrent Neural Network is proposed in this paper. The recurrent neural network adopted in this scheme is the Bilinear recurrent neural network (BRNN). Since the BRNN is based on the bilinear polynomial, BRNN has been successfully used in modeling highly nonlinear systems with time-series characteristics. Dynamic-BRNN (D-BRNN) further improves the convergence...
Bridge constriction in channels usually causes afflux which results in increase in backwater level well above the normal level and may possibly result in overflow on the flood plain surrounding the channel during flooding period. This paper uses Artificial Neural Network to predict the afflux based on the parameters including coefficient of frictions of main channel (nmc) and of floodplain (nfp),...
In the presentation major difficulties of designing neural networks are shown. It turn out that popular MLP (Multi Layer Perceptron) networks in most cases produces far from satisfactory results. Also, popular EBP (Error Back Propagation) algorithm is very slow and often is not capable to train best neural network architectures. Very powerful and fast LM (Levenberg- Marquardt) algorithm was unfortunately...
Estimation of Multi Input Multi Output (MIMO) channels can be performed by Artificial Neural Network (ANN)s such as Multi Layer Perceptron (MLP)s. However, the cost of training overload in case of time varying MIMO channels is the main bottleneck of such ANN architectures for which a viable alterative, namely, the Recursive Recurrent Network (RNN) is explored. Although for tightly coupled real and...
In this paper we proposed a new algorithm for neural network training. This algorithm is developed from modification on Levenberg-Marquardt algorithm for MLP neural network learning. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. We named this algorithm as GK-LM Method. An example is given here to show usefulness of this method. Finally...
This paper describes an application of the Orthogonal Least Squares (OLS) algorithm for feature selection of spoken letters. Traditionally used for system identification purposes, the OLS method was used to select important Mel-Frequency Cepstrum Coefficients (MFCC) for classification of two spoken letters - `A' and `S' using Multi-Layer Perceptron (MLP) neural network. We evaluated several network...
This paper investigates the performance of conjugate gradient algorithms with sliding-window approach for training multilayer perceptron (MLP). Online learning is implemented when the system under investigation is time varying or when it is not convenient to obtain a full history of offline data about the system variables. Sliding window framework is proposed to combine the robustness of offline learning...
This paper investigates the Malay speaker identification using Neural Networks. Speech database was developed with five speakers as trainers and five speakers as imposters. The speech training set included 30 vowel sounds of five trainer speakers. The test set included 30 vowel sounds from the five trainers and 30 vowel sounds from five imposters. The speech sounds were sampled at 20 kHz with 16 bit...
This work proposes to use Radial Basis Function - RBF artificial neural network and Multi-Layer Perceptron MLP with the algorithm cross-validation leave-one-out, to reduce the false-positives of suspicious regions automatically detected by a difference-of-Gaussian filter in mammography. This method was applied to 175 mammograms (one real lesion/image), from the Digital Database for Screening Mammography...
This paper describes the classification of different sample gases based on the dynamic responses of MOS based gas sensors using artificial neural network. The dynamic responses achieved by modulating the temperature profile were used for further analysis. Principal Component Analysis (PCA) was used to visualise the different sample gas patterns . Data classification was performed using supervised...
Traveling Wave Ultrasonic Motors (TWUSMs) possess extreme nonlinear properties such as saturation reverse effect and dead-zone, which are reliant on the driving conditions. These characteristics make modeling and control of TWUSMs highly challenging. Thus, deriving a simple and precise mathematical model suitable for controlling USMs has been a major problem for researchers. In this paper, a multi-layer...
This paper presents a new computer interface system based on laser points pattern recognition in beam projection, which can generate five interfacing commands. A new input and output mapping sensitive error back propagation (SBEP) algorithm for a multilayer neural network is proposed for successfully localizing laser spots in beam projection.
This paper suggests a methodology for segmentation of masses in digital mammograms. The masses are distinguished from other breast tissue by its homogeneous and differentiated density in relation to other breast tissues. The segmentation strategy is based on the assessment of density using multiscale wavelet transform. The density data obtained by processing with wavelet are used to train multilayer...
Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the illegal users is to monitoring these user's packets. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize...
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