Determination of Engine Misfire Location using Artificial Neural Networks

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Nouby M. Ghazaly ◽  
Muhammad Abdel-Fattah ◽  
Mostafa M. Makrahy
Keyword(s):  
Gradient Descent ◽  
Recognition Rate ◽  
Network Size ◽  
Training Algorithms ◽  
Spark Ignition Engines ◽  
Average Recognition Rate ◽  
Marquardt Algorithm ◽  
Gradient Descent Algorithm ◽  
Artificial Neural ◽  
Quasi Newton

Misfire in spark-ignition engines is one of the major faults that affect the power produced by the engine and pollute the environment and may cause further engine damage. This paper presents an evaluation of an artificial neural network based performance system through three most popular training algorithms namely Gradient Descent, Lavenberg-Marquadt and Quasi-Newton to determine the misfire location. Misfire is simulated by removing ignition coil to that cylinder namely Cylinder 1,2,3,4 and Cylinders 1 and 2, 1 and 4 and 2 and 3 with three different conditions such as idle, 2000 rpm and 3000 rpm. The results showed that the Quasi-Newton is higher in recognition rate average of 98.19 % but it takes more time to train. The Lavenberg-Marquardt algorithm is also good with an average recognition rate of 96.09 % with the fastest performance than Quasi-Newton. The gradient descent algorithm requires the network size to be more complicated to perform well with least time and high recognition rate.

scholarly journals Optical Recognition of Handwritten Logic Formulas Using Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2761
Author(s):  
Vaios Ampelakiotis ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis ◽  
George Tsihrintzis
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Gradient Descent ◽  
Feedforward Neural Networks ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Training Algorithms ◽  
Gradient Descent Algorithm ◽  
Two Stages ◽  
And Training

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.

scholarly journals Optimetric analysis of 1x4 array of circular microwave patch antennas for mammographic applications using adaptive gradient descent algorithm

2019 ◽  
Vol 9 (6) ◽  
pp. 5159
Author(s):  
Ojo O. Adedayo ◽  
Moses Oluwafemi Onibonoje ◽  
Ogunlade Michael Adegoke
Keyword(s):  
Neural Network ◽  
Patch Antenna ◽  
Breast Imaging ◽  
Gradient Descent ◽  
Ease Of Use ◽  
Microwave Antenna ◽  
High Price ◽  
Descent Algorithm ◽  
Marquardt Algorithm ◽  
Gradient Descent Algorithm

Interest in the use of microwave equipment for breast imagery is on the increase owing to its safety, ease of use and friendlier cost. However, some of the pertinent blights of the design and optimization of microwave antenna include intensive consumption of computing resources, high price of software acquisition and very large optimization time. This paper therefore attempts to address these concerns by devising a rapid means of designing and optimizing the performance of a 1×4 array of circular microwave patch antenna for breast imagery applications by deploying the adaptive gradient descent algorithm (AGDA) for a circumspectly designed artificial neural network. In order to cross validate the findings of this work, the results obtained using the adaptive gradient descent algorithm was compared with those obtained with the deployment of the much reported Levenberg-Marquardt algorithm for the same dataset over same frequency range and training constraints. Analysis of the performance of the AGDA neural network shows that the approach is a viable and accurate technique for rapid design and analysis of arrays of circular microwave patch antenna for breast imaging.

scholarly journals Predicting Hydraulic Conductivity (k) of Tropical Soils by using Artificial Neural Network (ANN)

2009 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Lim D.K.H ◽  
Kolay P.K.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Conjugate Gradient ◽  
Field Tests ◽  
Tropical Soils ◽  
Training Algorithms ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Hydraulic conductivity of tropical soils is very complex. Several hydraulic conductivity prediction methods have focused on laboratory and field tests, such as the Constant Head Test, Falling Head Test, Ring Infiltrometer, Instantaneous profile method and Test Basins. In the present study, Artificial Neural Network (ANN) has been used as a tool for predicting the hydraulic conductivity (k) of some tropical soils. ANN is potentially useful in situations where the underlying physical process relationships are not fully understood and well-suited in modeling dynamic systems on a real-time basis. The hydraulic conductivity of tropical soil can be predicted by using ANN, if the physical properties of the soil e.g., moisture content, specific gravity, void ratio etc. are known. This study demonstrates the comparison between the conventional estimation of k by using Shepard's equation for approximating k and the predicted k from ANN. A programme was written by using MATLAB 6.5.1 and eight different training algorithms, namely Resilient Backpropagation (rp), Levenberg-Marquardt algorithm (lm), Conjugate Gradient Polak-Ribiere algorithm (cgp), Scale Conjugate Gradient (scg), BFGS Quasi-Newton (bfg), Conjugate Gradient with Powell/Beale Restarts (cgb), Fletcher-Powell Conjugate Gradient (cgf), and One-step Secant (oss) have been compared to produce the best prediction of k. The result shows that the network trained with Resilient Backpropagation (rp) consistently produces the most accurate results with a value of R = 0.8493 and E2 = 0.7209.

scholarly journals Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction

2020 ◽  
Vol 20 (1) ◽  
pp. 20-33
Author(s):  
C. K. Arthur ◽  
V. A. Temeng ◽  
Y. Y. Ziggah
Keyword(s):  
Conjugate Gradient ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Ground Vibration ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Backpropagation Neural Network ◽  
Levenberg Marquardt ◽  
Quasi Newton ◽  
Bpnn Model

Abstract Backpropagation Neural Network (BPNN) is an artificial intelligence technique that has seen several applications in many fields of science and engineering. It is well-known that, the critical task in developing an effective and accurate BPNN model depends on an appropriate training algorithm, transfer function, number of hidden layers and number of hidden neurons. Despite the numerous contributing factors for the development of a BPNN model, training algorithm is key in achieving optimum BPNN model performance. This study is focused on evaluating and comparing the performance of 13 training algorithms in BPNN for the prediction of blast-induced ground vibration. The training algorithms considered include: Levenberg-Marquardt, Bayesian Regularisation, Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi-Newton, Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Fletcher-Powell Conjugate Gradient, Polak-Ribiére Conjugate Gradient, One Step Secant, Gradient Descent with Adaptive Learning Rate, Gradient Descent with Momentum, Gradient Descent, and Gradient Descent with Momentum and Adaptive Learning Rate. Using ranking values for the performance indicators of Mean Squared Error (MSE), correlation coefficient (R), number of training epoch (iteration) and the duration for convergence, the performance of the various training algorithms used to build the BPNN models were evaluated. The obtained overall ranking results showed that the BFGS Quasi-Newton algorithm outperformed the other training algorithms even though the Levenberg Marquardt algorithm was found to have the best computational speed and utilised the smallest number of epochs.   Keywords: Artificial Intelligence, Blast-induced Ground Vibration, Backpropagation Training Algorithms

Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks

2011 ◽  
Vol 217-218 ◽  
pp. 1458-1461
Author(s):  
J.P. Ren ◽  
R.G. Song
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Aluminum Alloy ◽  
Gradient Descent ◽  
Ferrous Metal ◽  
Network Control ◽  
Treatment Technique ◽  
Treatment Optimization ◽  
Gradient Descent Algorithm ◽  
Artificial Neural

In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation (BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduced by using gradient descent algorithms. A BP neural network has been established between the heat treatment technique and the hardness. The results indicated that the predicted results are closed to the test results. The weakness that the nonlinear and time variation relationship between heat treatment and the hardness could be approached more accurately, effectively by using single-factor-experiment method has been overcome. Hence providing a effective, economical，rapid way for the heat treatment optimization of nonferrous metals and ferrous metal.

scholarly journals Artificial Neural Network Optimization with Levenberg–Maruardt Algorithm for Dynamic Gesture Recognition

2018 ◽  
Vol 7 (3.13) ◽  
pp. 1
Author(s):  
Stephen John Dy ◽  
Matthew Adrianne Gonzales ◽  
Lenard Lozano ◽  
Miguel Angelo Suniga ◽  
Alexander Abad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gesture Recognition ◽  
Network Architecture ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Data Set ◽  
Marquardt Algorithm ◽  
Artificial Neural

Movement has long been a mode of expression and communication. A challenge arises when we try to bestow the ability to learn and recognize movements to machines, specifically computers, but with the development of sensor technology and the growing interest in machine learning algorithms, there is an opportunity to explore and formulate new approaches. The study focuses on the use of the Levenberg Marquardt Algorithm as an optimization algorithm for a multilayer Artificial Neural Network in constructing a predictive model for dynamic gestures. Extraction of the data set was made integral to the research. The study concludes that the network architecture is adequate for gesture recognition, with an average recognition rate of 83%, but a larger data set may show to improve this value.   

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Synthetic neural-like computing in microbial consortia for pattern recognition

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ximing Li ◽  
Luna Rizik ◽  
Valeriia Kravchik ◽  
Maria Khoury ◽  
Netanel Korin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gradient Descent ◽  
Biological Systems ◽  
Logic Gates ◽  
Structural Similarity ◽  
Microbial Consortia ◽  
Promoter Strength ◽  
Chemical Inducers ◽  
Artificial Neural

AbstractComplex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

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