Showing 7 results for Neural Networks
A Ayatollahi, N Jafarnia Dabanloo, Dc McLernon, V Johari Majd, H Zhang,
Volume 1, Issue 2 (4-2005)
Abstract
Developing a mathematical model for the artificial generation of
electrocardiogram (ECG) signals is a subject that has been widely investigated. One of its
uses is for the assessment of diagnostic ECG signal processing devices. So the model
should have the capability of producing a wide range of ECG signals, with all the nuances
that reflect the sickness to which humans are prone, and this would necessarily include
variations in heart rate variability (HRV). In this paper we present a comprehensive model
for generating such artificial ECG signals. We incorporate into our model the effects of
respiratory sinus arrhythmia, Mayer waves and the important very low frequency
component in the power spectrum of HRV. We use the new modified Zeeman model for
generating the time series for HRV, and a single cycle of ECG is produced using a radial
basis function neural network.
M. H. Sedaaghi,
Volume 5, Issue 1 (3-2009)
Abstract
Accurate gender classification is useful in speech and speaker recognition as
well as speech emotion classification, because a better performance has been reported when
separate acoustic models are employed for males and females. Gender classification is also
apparent in face recognition, video summarization, human-robot interaction, etc. Although
gender classification is rather mature in applications dealing with images, it is still in its
infancy in speech processing. Age classification, on the other hand, is also concerned as a
useful tool in different applications, like issuing different permission levels for different
aging groups. This paper concentrates on a comparative study of gender and age
classification algorithms applied to speech signal. Experimental results are reported for the
Danish Emotional Speech database (DES) and English Language Speech Database for
Speaker Recognition (ELSDSR). The Bayes classifier using sequential floating forward
selection (SFFS) for feature selection, probabilistic Neural Networks (PNNs), support
vector machines (SVMs), the K nearest neighbor (K-NN) and Gaussian mixture model
(GMM), as different classifiers, are empirically compared in order to determine the best
classifier for gender and age classification when speech signal is processed. It is proven that
gender classification can be performed with an accuracy of 95% approximately using
speech signal either from both genders or male and female separately. The accuracy for age
classification is about 88%.
M. Aghamohammadi, S. S. Hashemi, M. S. Ghazizadeh,
Volume 7, Issue 1 (3-2011)
Abstract
This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profile of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Bus voltage profile contains useful information about system stability margin including the effect of load-generation, line outage and reactive power compensation so, it is adopted as input pattern for VSANN. In fact, VSANN establishes a functionality for VSM with respect to voltage profile. Sensitivity analysis of VSM with respect to voltage profile and reactive power compensation extracted from information stored in the weighting factor of VSANN, is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select most effective buses for reactive power compensation aimed enhancing VSM. The proposed approach has been applied on IEEE 39-bus test system which demonstrated applicability of the proposed approach.
L. Ghods, M. Kalantar,
Volume 7, Issue 4 (12-2011)
Abstract
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.
A. Afrush, M. Shahriari-Kahkeshi,
Volume 15, Issue 2 (6-2019)
Abstract
This paper proposes an adaptive approximation-based controller for uncertain strict-feedback nonlinear systems with unknown dead-zone nonlinearity. Dead-zone constraint is represented as a combination of a linear system with a disturbance-like term. This work invokes neural networks (NNs) as a linear-in-parameter approximator to model uncertain nonlinear functions that appear in virtual and actual control laws. Minimal learning parameter (MLP) algorithm is proposed to decrease the computational load, the number of adjustable parameters, and to avoid the “explosion of learning parameters” problem. An adaptive TSK-type fuzzy system is proposed to estimate the disturbance-like term in the dead-zone description which further will be used to compensate the effect of the dead-zone, and it does not require the availability of the dead-zone input. Then, the proposed method based on the dynamic surface control (DSC) method is designed which avoids the “explosion of complexity” problem. Proposed scheme deals with dead-zone nonlinearity and uncertain dynamics without requiring the availability of any knowledge about them, and it develops a control input without singularity concern. Stability analysis shows that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to the vicinity of the origin. Simulation and comparison results verify the acceptable performance of the presented controller.
K. Zarrinnegar, S. Tohidi, M. R. Mosavi, A. Sadr, D. M. de Andrés,
Volume 19, Issue 1 (3-2023)
Abstract
The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural Network (MLP NN) and Convolutional Neural Network (CNN). Using the proposed method reduces data complexity, which can reduce the number of learning data requirements. The simulation results indicate that, by applying the proposed image processing algorithm for different dimensions of CAF images, the CNN performs better than MLP NN in terms of training accuracy; the MLP NN is superior to CNN in terms of convergence speed of training. In addition, the results demonstrate that the operation of the proposed method is appropriate in the case of small-delay spoofed signals. Therefore, for the intervals above 0.25 code chip, the proposed method detects spoofing attacks with a correct detection probability close to one.
M. Nezhadshahbodaghi, K. Bahmani, M. R. Mosavi, D. Martín,
Volume 19, Issue 2 (6-2023)
Abstract
Today, it can be said that in every field in which timely information is needed, we can use the applications of time-series prediction. In this paper, among so many chaotic systems, the Mackey-Glass and Loranz are chosen. To predict them, Multi-Layer Perceptron Neural Network (MLP NN) trained by a variety of heuristic methods are utilized such as genetic, particle swarm, ant colony, evolutionary strategy algorithms, and population-based incremental learning. Also, in addition to expressed methods, we propose two algorithms of Bio-geography-Based Optimization (BBO) and fuzzy system to predict these chaotic systems. Simulation results show that if the MLP NN is trained based on the proposed meta-heuristic algorithm of BBO, training and testing accuracy will be improved by 28.5% and 51%, respectively. Also, if the presented fuzzy system is utilized to predict the chaotic systems, it outperforms approximately by 98.5% and 91.3% in training and testing accuracy, respectively.
