Identification of bolt anchorage defects based on Elman neural network optimised by improved chicken swarm optimisation algorithm

2020 ◽  
Vol 62 (10) ◽  
pp. 588-597
Author(s):  
Weiguo Di ◽  
Mingming Wang ◽  
Xiaoyun Sun ◽  
Guang Han ◽  
Hui Xing
Keyword(s):  
Neural Network ◽  
Frequency Response ◽  
Network Models ◽  
Identification Accuracy ◽  
Response Functions ◽  
Neural Network Models ◽  
Deep Foundation ◽  
Elman Neural Network ◽  
Opposition Based Learning ◽  
Identification Model

Rock bolts play an important supporting role in the construction of slopes, deep foundation pits and tunnels. As such, it is especially necessary to assess bolt anchorage quality. This paper proposes an identification model for bolt anchorage defects based on an Elman neural network (ElmanNN) optimised using an improved chicken swarm optimisation (CSO) algorithm and the frequency response function. First, the principal components of the frequency response functions of different anchorage bolts are used as the input within the model. Next, the weights and thresholds of the ElmanNN are optimised using an improved CSO algorithm based on chaotic disturbance and elite opposition-based learning. Finally, the model is used to identify bolt anchorage defects. The experimental results show that the model has a higher identification accuracy and faster convergence rate than other neural network models.

Effect of Social Media Interactions on CLV in Telecommunications

2020 ◽  
Vol 19 (02) ◽  
pp. 447-468
Author(s):  
Oğuzhan Kivrak ◽  
Cüneyt Akar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Network Models ◽  
Neural Network Models ◽  
Telecommunication Sector ◽  
Elman Neural Network ◽  
Customer Lifetime ◽  
Twitter Account ◽  
Network Approaches ◽  
Artificial Neural Network Models

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

scholarly journals Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 22 ◽  
Author(s):  
Xingkui Xu ◽  
Chunfeng Wu ◽  
Qingyu Hou ◽  
Zhigang Fan
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Linear Models ◽  
Arima Model ◽  
Network Models ◽  
Random Component ◽  
Neural Network Models ◽  
Angle Sensor ◽  
Elman Neural Network ◽  
Differential Algorithm

As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.

scholarly journals Artificial Neural Network Models for Rainfall Prediction

2021 ◽  
Vol 5 (2) ◽  
pp. 30-35
Author(s):  
Emmanuel Gbenga Dada ◽  
Hurcha Joseph Yakubu ◽  
David Opeoluwa Oyewola
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Climatic Conditions ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Elman Neural Network ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Nonlinear Nature

Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.

BP Neural Network and its Improved Algorithm in the Power System Transformer Fault Diagnosis

2013 ◽  
Vol 418 ◽  
pp. 200-204
Author(s):  
Hao Qian Zhang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Bp Neural Network ◽  
Network Models ◽  
Gas Content ◽  
Neural Network Models ◽  
Bp Network ◽  
Transformer Fault ◽  
Network Pattern ◽  
Identification Model

According to the measured gas content in power transformers, we use BP neural network to accomplish the pattern recognition of transformer fault. The recognition effect of BP network pattern was studied from the aspects of adding over-fitting operation and genetic algorithm. Four kinds of neural network models, BP model BP & over-fitted identification model GABP model and GABP & over-fitted identification model, were constructed respectively, making the pattern recognition effect further enhanced.

scholarly journals Wood microscopic image identification method based on convolution neural network

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4986-4999
Author(s):  
Ziyu Zhao ◽  
Xiaoxia Yang ◽  
Zhedong Ge ◽  
Hui Guo ◽  
Yucheng Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Network Models ◽  
Identification Accuracy ◽  
Technical Solution ◽  
Wood Identification ◽  
Neural Network Models ◽  
Identification Method

To prevent the illegal trade of precious wood in circulation, a wood species identification method based on convolutional neural network (CNN), namely PWoodIDNet (Precise Wood Specifications Identification) model, is proposed. In this paper, the PWoodIDNet model for the identification of rare tree species is constructed to reduce network parameters by decomposing convolutional kernel, prevent overfitting, enrich the diversity of features, and improve the performance of the model. The results showed that the PWoodIDNet model can effectively improve the generalization ability, the characterization ability of detail features, and the recognition accuracy, and effectively improve the classification of wood identification. PWoodIDNet was used to analyze the identification accuracy of microscopic images of 16 kinds of wood, and the identification accuracy reached 99%, which was higher than the identification accuracy of several existing classical convolutional neural network models. In addition, the PWoodIDNet model was analyzed to verify the feasibility and effectiveness of the PWoodIDNet model as a wood identification method, which can provide a new direction and technical solution for the field of wood identification.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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