This paper proposes a novel adaptive decision feedback equalizer (DFE) based on self-constructing recurrent fuzzy neural network (SRFNN) for quadrature amplitude modulation systems. Without the prior knowledge of channel characteristics, a novel training scheme containing both selfconstructing learning and back-propagation algorithms is derived for the SRFNN. The proposed DFE is compared with s...

finger vein is one of the most fitting biometric for identifying individuals. in this paper a new method for finger vein recognition is proposed. first the veins are extracted from finger vein images by using entropy based thresholding. in finger vein images the veins are appeared as dark lines. the method extracts veins as well, but the images are noisy, that means in addition to the veins the...

Neural network (NN) tools are suitable for many tasks such as classification, clustering, scheduling and prediction. NN performance depends on the strategy of learning a phenomenon, the number of hidden nodes, activation function and, of course, the behavior of the data. There are many techniques used for training NN, while the social insect’s techniques become more focused by researchers becau...

Abstract The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG). EMG signal is being used in biomedical applications in order to detect abnormal muscle electrical activities that occur in many diseases and conditions like muscular dystrophy, inflammation of muscles, pinched nerves, peripheral nerve damages, amyotrophic lateral sclerosis, disc herniation, ...

Sign acquisition was mainly done using camera or sensor. Due to the invention of some advance devices like Leap Motion Sensor and Kinect the researchers have experienced new horizon for making Sign Language Recognition system more accurate. In this paper, an analysis of different Neural Networks for three sign languages is presented. Many experiments are performed for measuring the performance ...

In this paper we propose a Multilayer Perceptron Neural Network (MLP NN) consisting of fuzzy flip-flop neurons based on various fuzzy operations applied in order to approximate a real-life application, two input trigonometric functions, and two and six dimensional benchmark problems. The Bacterial Memetic Algorithm with Modified Operator Execution Order algorithm (BMAM) is proposed for Fuzzy Ne...

This article presents new neural network (NN) architecture to improve its ability for grade estimation. The main aim of this study is to use a specific NN which has a simpler architecture and consequently achieve a better solution. Most of the commonly used NNs have a fully established connection among their nodes, which necessitates a multivariable objective function to be optimized. Therefore...

We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers. The mathematical background for the modifications is given. In a benchmark application, the recognition of emotions in human speech, the accuracy of our F-SVM approa...

Problem statement: This study unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR). In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually ...

This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM[4], Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset[20] bymeasuring their classification test accuracy, and their sensitivity and specificity values. The said dataset consists...