scholarly journals Accuracy of Detection and Classification of DC Faults using Levenberg Marquardt Based Back Propagation Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 289-294
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Photovoltaic System ◽  
Back Propagation Algorithm ◽  
Pv System ◽  
Power Sources ◽  
Pv Module ◽  
Levenberg Marquardt ◽  
Sustainable Power

Vitality is seen as a prime administrator in the time of wealth and a vital figure budgetary headway. Obliged fossil resources and natural issues associated with them have underscored the necessity for new reasonable vitality supply choices that uses sustainable power sources. Among open developments for essentialness age from solar dependent sources, the photovoltaic system might give a gigantic pledge to develop a progressively feasible imperativeness structure. This paper presents accuracy of detecting DC faults in a photovoltaic (PV) framework based on Levenberg - Marquardt (LM) neural network. The result showed that this model based on LM neural network is effectual to grip doubts and nonlinearities of DC side faults in PV module without using mathematical model. All probable faults in DC side of PV system are obtained over 100 kW plant. It is discovered that the proposed framework has demonstrated its integrity for the pragmatic applications.

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

Application of a Multi-Structure Neural Network (MSNN) to Sorting Pistachio Nuts

1997 ◽  
Vol 08 (01) ◽  
pp. 55-61 ◽  
Author(s):  
Ahmad Ghazanfari ◽  
Anthony Kusalik ◽  
Joseph Irudayaraj
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Back Propagation ◽  
Classification Performance ◽  
Fourier Descriptor ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Pistachio Nuts ◽  
The Individual

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptor of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.

scholarly journals A Novel Adaptive Neural MPPT Algorithm for Photovoltaic System

2018 ◽  
Vol 15 (3) ◽  
pp. 5421-5434 ◽  
Author(s):  
S. A. Allahyari ◽  
Nasser Taheri ◽  
M. Zadehbagheri ◽  
Z. Rahimkhani
Keyword(s):  
Back Propagation ◽  
Photovoltaic System ◽  
Back Propagation Algorithm ◽  
Pv System ◽  
Maximum Power Point ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
And Control

This paper presents a novel adaptive neural network (ANN) for maximum power point tracking (MPPT) in photovoltaic (PV) systems under variable working conditions. The ANN-based MPPT model includes two separate NNs for PV system identification and control. NNs are trained by using of a novel back propagation algorithm in pre/post control phases. Because of online optimal performance of NNs, the proposed method, not only overcome the common drawbacks of the conventional MPPT methods, but also gives a simple and a robust MPPT scheme. Simulation results, which carried on MATLAB, show that proposed controller is the most effective in comparison with conventional MPPT approaches.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Performance assessment of free standing and building integrated grid connected photovoltaic system for southern part of India

2020 ◽  
pp. 014362442097774
Author(s):  
VS Chandrika ◽  
M Mohamed Thalib ◽  
Alagar Karthick ◽  
Ravishankar Sathyamurthy ◽  
A Muthu Manokar ◽  
...  
Keyword(s):  
Geographical Location ◽  
Photovoltaic System ◽  
Building Structures ◽  
System Efficiency ◽  
Pv System ◽  
Free Standing ◽  
Pv Module ◽  
Building Integrated Photovoltaic ◽  
Community Buildings ◽  
Equatorial Climate

Photovoltaic (PV) system efficiency depends on the geographical location and the orientation of the building. Until installing the building structures, the integration of the PV module must be evaluated with ventilation and without ventilation effects. This work optimises the performance of the 250 kWp grid-connected photovoltaic (GPV) for community buildings in the southern part of India. This simulation is carried out to evaluate the system efficiency of the GPV system under various ventilation conditions, such as free-standing PV (FSPV), building integrated photovoltaic ventilated (BIPV_V) and Building Integrated Photovoltaic without ventilation (BIPV). The PVsyst simulation tool is used to simulate and optimise the performance of the system with FSPV, BIPV and BIPV_V for the region of Chennai (13.2789° N, 80.2623° E), Tamilnadu, India. An annual system energy production is 446 MWh, 409 MWh and 428 MWh of FSPV, BIPV and BIPV_V system respectively. while electrical efficiency for the FSPV, BIPV_V, BIPV system is 15.45%. 15.25% and 14.75% respectively. Practical application: Integrating the grid connected photovoltaic system on the building reduces the energy consumption in the building. The integration of the PV on the roof or semi integrated on the roof is need to be investigated before installing on the buildings. The need for installation of the BIPV with ventilation is explored. This study will assist architects and wider community to design buildings roofs with GPV system which are more aesthetic and account for noise protection and thermal insulation in the region of equatorial climate zones.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

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