Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

Back Propagation ◽

Problem Size ◽

Functional Link ◽

Optimal Weights ◽

Feed Forward Network ◽

Higher Order Neural Networks ◽

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

Journal of Electrical Engineering ◽

10.2478/v10187-010-0033-4 ◽

2010 ◽

Vol 61 (4) ◽

pp. 235-240 ◽

Cited By ~ 5

Author(s):

Perumal Chandrasekar ◽

Vijayarajan Kamaraj

Keyword(s):

Neural Network ◽

Neural Networks ◽

Power Quality ◽

Wavelet Transforms ◽

Back Propagation ◽

Voltage Sag ◽

Artificial Neural ◽

Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Archives of Environmental Protection ◽

10.1515/aep-2017-0034 ◽

2017 ◽

Vol 43 (4) ◽

pp. 26-32 ◽

Cited By ~ 2

Author(s):

Sinan Mehmet Turp

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Back Propagation ◽

Data Sets ◽

Adsorption Efficiency ◽

Back Propagation Algorithm ◽

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

New Method for Locker Door with Face Security System Using Backpropagation Algorithm

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.13.16931 ◽

2018 ◽

Vol 7 (2.13) ◽

pp. 402

Author(s):

Y Yusmartato ◽

Zulkarnain Lubis ◽

Solly Arza ◽

Zulfadli Pelawi ◽

A Armansah ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Human Brain ◽

Back Propagation ◽

The Face ◽

Artificial Neural ◽

Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.

Back Propagation Method of Artificial Neural Networks for Finding the Position Control of Stanford Manipulator and Direct Kinematic Analysis of Elbow Manipulator

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v7i1.23 ◽

2018 ◽

Vol 7 (1) ◽

pp. 46 ◽

Cited By ~ 1

Author(s):

M. Sailaja ◽

R. D. V. Prasad

Keyword(s):

Neural Network ◽

Neural Networks ◽

Inverse Kinematics ◽

Back Propagation ◽

Network Approach ◽

Neural Network Approach ◽

Artificial Neural ◽

Direct Kinematics

Nowadays the robot technology is advancing rapidly and the use of robots in industries has been increasing. In designing a robot manipulator, kinematicsplays a vital role. The kinematic problem of manipulator control is divided into two types, direct kinematics and inverse kinematics. Robot inverse kinematics, which is important in robot path planning, is a fundamental problem in robotic control. Past solutions for this problem have been through the use of various algebraic or algorithmic procedures, which may be less accurate and time consuming. Artificial neural networks have the ability to approximate highly non-linear functions applied in robot control. The neural network approach deserves examination because of the fundamental properties of computation speed, and they can generalize untrained solutions. In the present work an attempt has been made to evaluate the problemof robot inverse kinematics of Stanford manipulator using artificial neural network approach. Finally two programs are written using C language to solve inverse kinematic problem of Stanford manipulator using Back propagation method of artificial neural network. In this network, the input layer has six nodes, the hidden layer has three nodes, and the output layer has two nodes. And also Elbow manipulator was modelled and its direct kinematics was analysed.

Use of Artificial Neural Networks for Calculating Elasticity Modulus of Construction Layers of Non-Rigid Road Surfacing Based on Natural Data

НАУЧНЫЙ ЖУРНАЛ СТРОИТЕЛЬСТВА И АРХИТЕКТУРЫ ◽

10.36622/vstu.2020.58.2.009 ◽

2020 ◽

pp. 111-120

Author(s):

М. М. М. Елшами ◽

А. Н. Тиратурян ◽

А. Н. Канищев

Keyword(s):

Neural Network ◽

Neural Networks ◽

Elastic Moduli ◽

Back Propagation ◽

Data Set ◽

Artificial Neural ◽

Instrumental Recording ◽

Accuracy Of Prediction

Постановка задачи. Рассматриваются вопросы использования искусственных нейронных сетей при решении задач обработки результатов инструментальных регистраций чаш прогибов нежесткой дорожной одежды с использованием установок ударного нагружения FWD . Результаты. Проведен анализ и отмечены недостатки существующих методов обработки экспериментальных чаш прогибов, в частности метода обратного расчета модулей упругости слоев дорожных одежд, заключающиеся в длительном времени выполнения расчетов и неустойчивости получаемых результатов. Построена структура искусственной нейронной сети для определения модулей упругости слоев дорожной одежды. Обучение искусственной нейронной сети осуществлялось с использованием метода обратного распространения ошибки. Выводы. Разработанная нейронная сеть продемонстрировала хорошие результаты при обучении по тестовому набору данных, а также высокую точность прогнозирования модулей упругости слоев дорожных одежд. Statement of the problem. The article is devoted to the use of artificial neural networks in solving the problems of processing the results of instrumental recording of bowls of deflections of non-rigid road surfacing using FWD shock loading settings. Results. The analysis was carried out, the shortcomings of the existing processing methods were identified, in particular the backcalculation method, which involves a long calculation time, and the instability of the results obtained. The structure of the artificial neural network was designed to determine the elastic moduli of the pavement layers. Training of an artificial neural network was carried out using the method of back propagation of error. Conclusions. The developed neural network has shown good results in training on the test data set, as well as high accuracy of prediction of the elastic moduli of the pavement.

Artificial Neural Network Modelling for Waste

Computational Modeling and Simulation of Intellect ◽

10.4018/978-1-60960-551-3.ch010 ◽

2011 ◽

pp. 224-263 ◽

Cited By ~ 2

Author(s):

Eldon R. Rene ◽

M. Estefanía López ◽

María C. Veiga ◽

Christian Kennes

Keyword(s):

Neural Network ◽

Neural Networks ◽

Wastewater Treatment ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Network Modelling ◽

Waste Gas ◽

Propagation Algorithm ◽

Due to their inherent robustness, artificial neural network models have proven to be successful and have been used extensively in biological wastewater treatment applications. However, only recently, with the scientific advancements made in biological waste gas treatment systems, the application of neural networks have slowly gained the practical momentum for performance monitoring in this field. Simple neural models, after vigorous training and testing, are able to generalize the results of a wide range of operating conditions, with high prediction accuracy. This chapter gives a fundamental insight and overview of the process mechanism of different biological waste gas (biofilters, biotrickling filters, continuous stirred tank bioreactors and monolith bioreactors), and wastewater treatment systems (activated sludge process, trickling filter and sequencing batch reactors). The basic theory of artificial neural networks is explained with a clear understanding of the back propagation algorithm. A generalized neural network modelling procedure for waste treatment applications is outlined, and the role of back propagation algorithm network parameters is discussed. Anew, the application of neural networks for solving specific environmental problems is presented in the form of a literature review.

River flow forecasting using different artificial neural network algorithms and wavelet transform

Canadian Journal of Civil Engineering ◽

10.1139/l08-090 ◽

2009 ◽

Vol 36 (1) ◽

pp. 26-38 ◽

Cited By ~ 41

Author(s):

Turgay Partal

Keyword(s):

Neural Network ◽

Neural Networks ◽

Wavelet Transform ◽

Back Propagation ◽

Propagation Model ◽

River Flows ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

In this study, the wavelet–neural network structure that combines wavelet transform and artificial neural networks has been employed to forecast the river flows of Turkey. Discrete wavelet transforms, which are useful to obtain to the periodic components of the measured data, have significantly positive effects on artificial neural network modeling performance. Generally, the feed-forward back-propagation method was studied with respect to artificial neural network applications to water resources data. In this study, the performance of generalized neural networks and radial basis neural networks were compared with feed-forward back-propagation methods. Six different models were studied for forecasting of monthly river flows. It was seen that the wavelet and feed-forward back-propagation model was superior to the other models in terms of selected performance criteria.

Damage Identification of Rock Mass with Artificial Neural Networks

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.353-358.2325 ◽

2007 ◽

Vol 353-358 ◽

pp. 2325-2328

Author(s):

Zi Chang Shangguan ◽

Shou Ju Li ◽

Mao Tian Luan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Damage Identification ◽

Back Propagation ◽

Training Time ◽

Marquardt Algorithm ◽

Artificial Neural ◽

High Degree

The inverse problem of rock damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Convergence measurements of displacements at a few of positions are used to determine the location and magnitude of the damaged rock in the excavation disturbed zones. Unlike the classical optimum methods, ANN is able to globally converge. However, the most frequently used Back-Propagation neural networks have a set of problems: dependence on initial parameters, long training time, lack of problemindependent way to choose appropriate network topology and incomprehensive nature of ANNs. To identify the location and magnitude of the damaged rock using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.

Feature Selection Using Evolutionary Functional Link Neural Network for Classification

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v6.i4.pp359-367 ◽

2017 ◽

Vol 6 (4) ◽

pp. 359

Author(s):

Amaresh Sahu ◽

Sabyasachi Pattnaik

Keyword(s):

Neural Network ◽

Learning Algorithm ◽

Back Propagation ◽

Computational Time ◽

Classification Problems ◽

Hybrid Functional ◽

Functional Link ◽

Artificial Neural ◽

Functional Link Neural Network

<p>Computational time is high for Multilayer perceptron (MLP) trained with back propagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to train, and gives better result in the classification problems. The paper proposed an evolutionary functional link artificial neural network (EFLANN) using genetic algorithm (GA) by eliminating features having little or no predictive information. Particle swarm optimization (PSO) is used as learning tool for solving the problem of classification in data mining. EFLANN overcomes the non-linearity nature of problems by using the functionally expanded selected features, which is commonly encountered in single layer neural networks. The model is empirically compared to MLP, FLANN gradient descent learning algorithm, Radial Basis Function (RBF) and Hybrid Functional Link Neural Network (HFLANN) . The results proved that the proposed model outperforms the other models.</p>

A Single Hidden Layer Artificial Neural Network Model that Employs Algebraic Neurons: Algebraic Learning Machine

10.21203/rs.3.rs-351062/v1 ◽

2021 ◽

Author(s):

Ömer Faruk Ertuğrul

Keyword(s):

Neural Network ◽

Back Propagation ◽

Mathematical Operation ◽

Functional Link ◽

Neural Network System ◽

Benchmark Datasets ◽

Artificial Neural ◽

Learning Machine ◽

Hidden Layer

Abstract Artificial neural networks (ANN) have been employed successfully because of their high modeling capability. Many versions of the ANN have been proposed to increase the modeling ability. Since ANN based on the biological neural network system, the only mathematical operation is summation or subtraction (while the coefficients are negative). This research was done to investigate the application of other mathematical operations, which are multiplication, division, logarithm, and exponential, in nodes. Based on this fact, a novel a single hidden layer feed-forward artificial neural network (SLFN) model, which was called was algebraic learning machine (ALM), was proposed. The proposed ALM was evaluated and validated with 60 different benchmark datasets. Obtained results were compared with results obtained by each of the extreme learning machine (ELM), randomized artificial neural network, and random vector functional link, and back-propagation trained SLFN methods. Achieved results show that the proposed method is successful enough to be employed in classification and regression.