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The back-propagation algorithm has been used widely as a learning algorithm in a feed-forward multilayer neural network. In this study, fault detection was carried out using the information of the arc current. After collecting the actual data, wavelet transformation were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The...
In this paper two different neural network architectures are investigated for enough accurate field strength prediction in the complex indoor environment. The investigation includes multilayer perceptron (MLP) and radial basis function (RBF) neural networks. It has been already shown for neural networks as powerful tool in RF propagation prediction. Standard empirical or deterministic field prediction...
In recent years reflectarray antennas have become very popular research area for scientists to overcome their limitations which are the narrow band of the radiating elements and the differential spatial phase delay between elements on the reflectarray. The recent aim of reflectarray antenna design is to have a smaller gradient (slower slope) of the reflected wave phase by varying the size of radiating...
The performance comparison between two controllers, namely Adaptive Neuro-Controller (ANC), based on Multi Layered Perceptron (MLP) network and Adaptive Parametric Black Box Controller (APBBC) are presented in this paper. The comparison is based on the capability of the controlled output tracking the model reference output and the percentage of overshoot. Both controllers are based on a black box...
A novel speech recognition method has been proposed which combines the capabilities of a Recurrent Fuzzy Multilayer Perceptron (MLP) to the existing Mel Frequency Cepstral Coefficients (MFCC) model, synthesized using JAVA. Performance analysis of the proposed recurrent fuzzy MLP relative to a speech recognition system has been shown using MATLAB. Owing to its short-term memory effect in addition to...
In this paper we proposed an automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia disease using standard 12 lead ECG signal recordings. In this study, we are mainly interested in classifying different arrhythmia types (classes) using multilayer peceptron (MLP) model. We have used UCI ECG signal data to train and test MLP network model. For this multi class classification...
This paper focuses on enhancing MFCC features using a set of MLP NN in order to improve phoneme recognition accuracy under different noise types and SNRs. A NN can be used in different domains (between any pair of MFCC feature extraction blocks). It includes FFT, MEL, LOG, DCT and DELTA domains. Various domains have different complexities and achieve different degrees. A comparative study is done...
This paper presents the classification of ionic concentration using ion-sensitive field-effect transistor (ISFET) sensors with post-processing neural network ensemble. ISFETs are electrochemical potentiometric sensors that produce voltage response indicative of ionic concentration change. However, in the presence of ions of similar charge, the voltage levels tend to be influenced by the interfering...
It is important to study the neural network (NN) when it falls into chaos, because brain dynamics involve chaos. In this paper, the several chaotic behaviors of supervised neural networks using Hurst Exponent (H), fractal dimension (FD) and bifurcation diagram are studied. The update rule for NN trained with back-propagation (BP) algorithm absorbs the function of the form x(1-x) which is responsible...
This paper presents a comparison between multilayer perceptron neural network (MLP NN) and Radial basis feedforward neural network (RBF NN) techniques for the detection of inter-turn short circuit fault in stator winding of an induction motor. The fault location process is based on the monitoring the three phase shifts between the line current and the phase voltage of the induction machine.
This paper presents a face recognition method based on correspondence analysis (CA) and trained artificial neural network. In this algorithm, features are extracted using CA, then these features are fed to Multi layer Perceptron (MLP)network for classification and finally, after training the network, effective features are selected with UTA algorithm. The obtained experimental results indicate high...
Welding is an unavoidable unit practically for every manufacturing industry. Electron Beam welding (EBW) are very important unit of some specific manufacturing processes where high degree of accuracy and flawless welding is highly desirable like aerospace engineering. The power supply unit (PSU) used for EBW are very important unit, for which high degree of stability and accuracy is a must. Since...
Cormack classification is believed as a golden indicator for predicting tracheal intubation is difficult or not in clinic. Some anaesthetists usually estimate the airway state by examining single airway features. However, specialists agree that prediction accuracy of a difficult airway may be improved if multiple static and dynamic metrical airway features were considered. In this paper, we developed...
Up to now, various detection algorithms have been offered and investigated for DS-CDMA systems in multipath conditions. Here, We intend to implement sub-optimum receivers based on Maximum-Ratio-Combining (MRC) via neural network structures in Downlink systems. We will demonstrate that our design based on Radial Base function (RBF) and Multi Layer Perceptron (MLP) outperform in comparison of conventional...
Navigation systems used in recent days rely mainly on Kalman filter to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In common, INS/GPS data fusion provides reliable navigation solution by overcoming drawbacks such as signal blockage for GPS and increase in position errors with time for INS. Kalman filtering INS/GPS integration techniques used in present...
This paper presents a novel architecture for the FPGA-based implementation of multilayer neural network (NN), which integrates the layer-multiplexing and pipeline architecture together. The proposed method is aimed at enhancing the efficiency of resource usage and improving the forward speed at the module level, so that a larger NN can be implemented on commercial FPGAs. We developed a mapping method...
Steganographic techniques are being applied across a broad set of different digital technologies. The steganographic method will be used for internet/network security, watermarking and so on. So, the steganography is the process of hiding one medium of communication (Text, Sound, and Image) within another. It can work on JPEG 2000 compressed images & stir Mark images. The new method of steganalysis...
This paper describes a Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of two stages: i) a multilayer neural network (MLN), which converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities and ii) the phoneme probabilities obtained from the first stage and corresponding Δ and ΔΔ are inserted into another MLN to...
This paper presents a method for extracting distinctive phonetic features (DPFs) for automatic speech recognition (ASR). The method comprises three stages: i) a acoustic feature extractor, ii) a multilayer neural network (MLN) and iii) a hidden Markov model (HMM) based classifier. At first stage, acoustic features, local features (LFs), are extracted from input speech. On the other stage, MLN generates...
An important problem in channel equalization, is that of, removing distortions introduced by linear or nonlinear message corrupting mechanisms in the reconstruction of the original signals. Severe nonlinear distortions can make it difficult for conventional equalizers to reconstruct the original signals. In this paper, we propose a Decision Feedback Equalizer which can recover the original signals...
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