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14 articles for “Back-propagation learning neural networks”
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Audio-Only Speaker Identification using Principal Component Analysis based Back-Propagation Learning Neural Network in Noisy Environment
Abstract: This paper introduces text dependent speaker identification system on Principal Component Analysis based Back-Propagation learning neural network which deals with detecting a particular speaker from a known populations under noisy environment. For audio pre-processing, ends point detection, silence parts removal, frame segmentation and windowing techniques have been used and wiener filter has been applied to remove the background noise from the audio speech utterances. To reduce the dimension of the …
Published in Current Trends in Signal Processing · Vol. 3, Issue 3, 2013 · pp. 1–10 Read article
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Iris Recognition System Using Principal Component Analysis based Back-Propagation Learning Neural Networks
Abstract: This paper deals with the iris recognition system using Back-Propagation learning neural network algorithm where Principal Component Analysis technique has been used to reduce the dimension of the iris feature vector. Automated iris localization and segmentation methods have applied to effectively isolate the iris region from pupil and sclera. Circular Hogue transform has used to detect the iris/sclera boundary and pupil/iris boundary. To remove the upper and lower eyelids effects …
Published in Current Trends in Information Technology · Vol. 3, Issue 3, 2013 · pp. 5–11 Read article
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Decision Fusion based Pair of Iris Recognition using Back-Propagation Learning Neural Network Algorithm
Abstract: AbstractThe contribution of this work is to enhance the performance of the iris recognition system through decision fusion of left and right iris pattern. Iris recognition system performs well and identify human correctly in neutral environment. In this paper a pair of iris recognition system has been proposed, which is capable enough to identify human through noisy environments. Principal component analysis based dimensionality reduction technique has been used toreduce and …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 2, 2015 · pp. 1–6 Read article
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Multi-Layer Perceptron Neural Network Based Person Identification Using Appearance Based Facial Feature
Abstract: This paper deals with secure person identification system using Back-Propagation learning neural network algorithm where appearance based facial feature and Principal Component Analysis based dimensionality reduction technique have been used. To extract the appearance based facial features, Viola-Jones method has used as a face detector, Stam’s method of Active Shape Model has applied for detecting the facial edges and image pre-processing method has used for eliminating the background noises. For …
Published in Current Trends in Signal Processing · Vol. 4, Issue 1, 2014 · pp. 26–34 Read article
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Performance Comparison between Back-Propagation Learning and Kohonen Self-Organizing Neural Networks Algorithm in Terms of Pattern Recognition
Abstract: Pattern recognition using back-propagation learning and Kohonen self-organizing neural network algorithms has been developed and measured various performance based on different criteria and environment of the pattern. These pattern recognition systems have taken the object image as input. In image pre-processing stage, scaling and clipping process has been applied from the background image to avoid unnecessary portion of the object image. Feature extraction has been performed after applying filtering and …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 1, Issue 1, 2014 · pp. 1–8 Read article
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Retina Recognition System Using Wavelet based Neural Network Algorithm
Abstract: This paper presents an approach of wavelet feature-based retina recognition system using back-propagation learning neural network algorithm. After acquiring the retinal image, at first vessels were segmented from the image. Then the feature extraction was carried out by analyzing the segmented retinal image using multiresolution analysis through the wavelet-based approach. Then extracted features were fed to the back-propagation learning neural network algorithm to create the learned template which was used …
Published in Current Trends in Signal Processing · Vol. 5, Issue 2, 2015 · pp. 35–39 Read article
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Nonlinear Semiconductor Device Modeling using Neural Networks
Abstract: This paper describes the nonlinear semiconductor (transistor) of small and large signal modeling using a single neural network. Multilayer perceptron (MLP) with back-propagation (BP) learning is adopted in this work to model the drain current (ID) and the transconductance (gm) of the transistor. MLP modeling performance in terms of mean square error (MSE) and complexity of the network are illustrated briefly. Artificial neural network (ANN) model outcome for nonlinear function …
Published in Journal of VLSI Design Tools and Technology · Vol. 4, Issue 3, 2014 · pp. 1–6 Read article
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Managing the Resources of LTE Networks using Multi-orthogonal Access based on Deep Learning
Abstract: AbstractOne of the topics discussed in telecommunications systems is joint subcarrier and power allocation in the uplink of an NOMA system that we study. Due to this reason a novel radio resource management framework is presented based on code-domain and a deep learning algorithm for uplink and downlink transmissions, such that the neural network is trained by Bayesian regularization back propagation and the mean squared error )MSE) are the training …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 7, Issue 2, 2020 · pp. 19–26 Read article
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Prediction of Human Heart Disease
Abstract: AbstractData mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 6, Issue 2, 2019 · pp. 27–31 Read article
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Mechatronics Robot Navigation using Machine Learning through Prolog Programming Language
Abstract: A basic decision-making system is developed in this paper using Neural Network in Machine learningto explore a robot in concealed condition. The robot can move out of explicit labyrinths effectivelythrough modifying its bearing and speed persistently via the neural system model for machinelearning. Over the past several years, navigation tasks for mobile robots have been widely studied.There have been many attempts to introduce the usage of machine learning algorithms. Excellentperformance …
Published in Journal of Mechatronics and Automation · Vol. 8, Issue 1, 2021 · pp. 39–47 Read article
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Design of Beam using Artificial Neural Network based Approach
Abstract: Recent developments in artificial neural network (ANN) have opened up new possibilities in the field of structural engineering. This paper demonstrates the applicability of ANN for the design of beams subjected to moment and shear. An attempt has been made to capture the mapping between the design variables using ANN. There is no direct method for design of beams. A feed forward network and back propagation training algorithm has been …
Published in Recent Trends in Civil Engineering & Technology · Vol. 4, Issue 3, 2014 · pp. 1–6 Read article
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Simulation of Neural Network based PID Controller for Pressure Process
Abstract: This paper provides a Neural Network PID controller based on Back Propagation (BP) algorithm applied to pressure control in a tank. The controller has many advantages like that more convenient in parameter regulating, better robust. Neural network is to adjust the parameters of PID controller based on the operational status of the system, to achieve a better performance, making the output of the output neurons corresponding to the three adjustable …
Published in Journal of Control & Instrumentation · Vol. 4, Issue 1, 2013 · pp. 23–27 Read article
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Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations
Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 39–46 Read article
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Analysis Of Gradient Descent In Back Propagation Neural Network For Maximum And Efficient Utilization
Abstract: The back-propagation is a successfully established algorithm for multi-layered perceptron neural networks, which is usuallywith successfully for tiny network architectures or small tasks. In this paper we have highlighted the important libraries for thepurpose of implementation of neural networks. After that we have given the process of feed forward in neural network and howwe optimize this process by updating the weights by going backward towards the previous layers. The Author …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 9, Issue 1, 2022 · pp. 21–30 Read article