scholarly journals Short term prediction of atmospheric temperature using neural networks

MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 471-480
Author(s):  
S. PAL ◽  
J. DAS ◽  
P. SENGUPTA ◽  
S. K. BANERJEE
Keyword(s):  
Neural Network ◽  
Minimum Temperature ◽  
Ground Level ◽  
Test Sample ◽  
Atmospheric Temperature ◽  
Training Sample ◽  
Test Cases ◽  
Backpropagation Method ◽  
Hidden Layer ◽  
Short Term Prediction

In this paper, a neural network based forecasting model for the maximum and the minimum temperature for the ground level is proposed. A backpropagation method of gradient-decent learning in multi-layer perceptron (MLP) type of neural network with only one hidden layer is considered. This network consists of 25 input nodes and two output nodes. The network is trained with a varying number of nodes in the hidden layer using a set of training sample and each of them is tested with a set of test sample. It accepts previous two consecutive days information (such as pressures, temperatures, relative humidities, etc.) as inputs for the estimation of the maximum and the minimum temperature as output. The network with 20 or less neurons in the hidden layer is found to be "optimum" and it produces an error within ±2° C for 80% of test cases.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 894 ◽  
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Jingjing Li ◽  
Yong Zhu ◽  
Peiyao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Training Sample ◽  
Accuracy Rate ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Axial Piston ◽  
Sample Set

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.

A Review of Neural Network Application for Fault Diagnosis of NPP

2009 ◽  
Author(s):  
He Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Layer Structure ◽  
Engineering Application ◽  
Model Performance ◽  
Training Sample ◽  
Feed Forward Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Artificial Neural Network (ANN) with its self-learning capabilities, nonlinear mapping ability and generalization ability, has been widely applied for fault diagnosis of complex system like Nuclear Power Plant (NPP). In this paper, an overview of the application of supervised multi-layer feed-forward neural network for fault diagnosis of NPP is presented, including the following aspects: the acquisition of the training sample data, the determination of appropriate input and output data, the choice of hidden layer structure and the evaluation of network model performance. Finally, a number of key issues about the engineering application of neural network fault diagnosis in practice were discussed.

Study of Digital Character Recognition Based on BP Neural Networks

2013 ◽  
Vol 333-335 ◽  
pp. 856-859 ◽  
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Solution Space ◽  
Training Sample ◽  
Back Propagation Algorithm ◽  
Sample Object ◽  
Training Error ◽  
Hidden Layer

A digital character recognition method is presented based on BP Neural Network. This paper preprocesses the digital character image and extracts character feature, then uses BP Neural Network to recognize digital character. Back Propagation algorithm seeks network weights to minimize training error in the solution space. A network with hidden layer is created at first, then an input sample vector is sent to network input terminal and the square error E between output values and training sample object output values is calculated. Above process is repeated for input samples of training sets until the error is reduced within the limits of the threshold. The results show that the method presented has good accuracy, quick speed and strong robustness for realtime application.

scholarly journals Prediksi Tingkat Pertumbuhan Ekonomi Provinsi Sulawesi Tengah Menggunakan Metode Backpropagation

2020 ◽  
Vol 16 (2) ◽  
pp. 126-134
Author(s):  
I M Jafaar ◽  
A Sahari ◽  
D Lusiyanti
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Regional Economy ◽  
Backpropagation Neural Network ◽  
Network Methods ◽  
Neural Network Prediction ◽  
Backpropagation Method ◽  
Network Prediction ◽  
Hidden Layer ◽  
Central Sulawesi

ABSTRACTEconomic growth in the region is the regional economy conditions change continuously towards a better State fora certain period. The slow economic growth became the latest leading indicator an area to develop. Indicators thatcan be used for example, GDP and inflation. On the research of these indicators will be used to predict the growthrate of the economy of Central Sulawesi province using the Backpropagation Neural Network Methods. Simulationof the program in the form of input data is represented 𝑥1 and 𝑥2 and biased 𝑏1 dan 𝑏2 symbolized. With hiddenlayers comprising 𝑧1, 𝑧2, 𝑧3, 𝑧4, … , 𝑧17 . and y as output. Based on the results and discussion has been done, can bedrawn the conclusion of process Neural Network prediction of Backpropagation with 1 hidden layer neurons andthe number 17 against 26 data represents data inflation and GDP of the year 2010 up to 2016 with sigmoid activationfunction binner was able to predict the rate of economic growth with a prediction error of 16.66%.Keywords : ANN, Backpropagation Method, Inflation, PDRB.

scholarly journals THE NEURAL NETWORK MODEL OF INDIVIDUALS CREDIT RATING

2019 ◽  
Vol 8 (6) ◽  
Author(s):  
Ilyas I. Ismagilov ◽  
Linar A. Molotov ◽  
Alexey S. Katasev ◽  
Dina V. Kataseva
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Rating ◽  
Test Sample ◽  
Training Sample ◽  
Consumer Lending ◽  
The Neural Network ◽  
Intelligent Decision Support Systems ◽  
Model Training

This article solves the problem of constructing and evaluating a neural network model to determine the creditworthiness of individuals. It is noted that the most important part of the modern retail market is consumer lending. Therefore, an adequate and high-quality assessment of the creditworthiness of an individual is a key aspect of providing credit to a potential borrower. The theoretical and practical aspects of assessing the creditworthiness of individuals are considered. To solve this problem, the need for the use of intelligent modeling technologies based on neural networks is being updated. The construction of a neural network model required the receipt of initial data on borrowers. Using correlation analysis, 14 input parameters were selected that most significantly affect the output. The training and test data samples were generated to build and evaluate the adequacy of the neural network model. Training and testing of the neural network model was carried out on the basis of the analytical platform “Deductor”. Analysis of contingency tables to assess the accuracy of the neural network model in the training and test samples showed positive results. The error of the first kind on the data from the training sample was 0.45%, and the error of the second kind was 1.39%. Accordingly, the error of the first kind was not observed on the data from the test sample, and the error of the second kind was 2.68%. The results obtained indicate a high generalizing ability and adequacy of the constructed neural network, as well as the possibility of its effective practical use as part of intelligent decision support systems for granting loans to potential borrowers

scholarly journals Classification of brain tumors using the multilayer perceptron artificial neural network

2018 ◽  
Vol 16 (36) ◽  
pp. 190-198
Author(s):  
Raid Adnan Omar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Tumor ◽  
Multilayer Perceptron ◽  
Research Work ◽  
Test Sample ◽  
Training Sample ◽  
Statistical Characteristics ◽  
Correct Classification ◽  
Artificial Neural

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect on how the network perform when predicting cases of brain tumor, contrast accounted for 64.3 %, correlation accounted for 56.7 %, and entropy accounted for 54.8 %. All remaining characteristics accounted for 21.3-46.8 % of normalized importance. The output of the neural networks showed that sensitivity and specificity were scored remarkably high level of probability as it approached % 96.

scholarly journals Ultra-short-term Radiation Prediction based on FY-4 Satellite Cloud Images and Artificial Neural Network

2021 ◽  
Vol 293 ◽  
pp. 03017
Author(s):  
Dongyu Jia ◽  
Xiaoying Nie ◽  
Fuyuan Gao ◽  
Qingfeng Li
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Test Sample ◽  
Surface Radiation ◽  
Short Term ◽  
Surface Solar Radiation ◽  
Random Mutation ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Combined Algorithm

Surface solar radiation is affected by many random mutation factors, which makes the ultra-short-term prediction face great challenges. In this paper, the surface radiation observation station in the northwest (Dunhuang) desert area with broad PV prospects is selected as the research object. The input parameters of the test sample are: cloud forecast value, reflectivity and brightness temperature value of a satellite cloud image closest to the forecast time. The MATLAB software is used to model the prediction program and to predict the surface solar radiation in the next 10 minutes. A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation. The model is a lightweight calculation model, it satisfies the calculation precision of engineering requirements. The results show that the diurnal variation trend of measured and predicted radiation values is basically the same. Among them, the prediction accuracy of the model for cloudy days is higher, while for snowy days with more abrupt changes, the prediction error of abrupt points is larger. The model can provide reference for ultra-short-term prediction of surface radiation.

scholarly journals Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244029
Author(s):  
Mohamed Mansour ◽  
Saleh Alsulamy ◽  
Shaik Dawood
Keyword(s):  
Neural Network ◽  
Test Sample ◽  
Training Sample ◽  
International Standards ◽  
Iso 14001 ◽  
Economic Activities ◽  
Neural Network Approach ◽  
Industrial Organizations ◽  
Iso 14001 Certification ◽  
Environmental Performance Evaluation

The purpose of this study was to model the link between the implementation of ISO 14031 and ISO 14001. This study connects ISO 14031’s guidelines as independent variables to a dependent variable expressed by the ISO 14001 certification situation of industrial organizations based on the judgments of environmental managers in Saudi Arabia. Applying the quantitative approach using a survey with 596 responses from organizations functioning in 30 economic activities, a multi-layered neural network was trained to examine the relationship between standards and predict whether the organization is ISO 14001 certified in addition to testing the developed network on a group of collected cases. The results demonstrated the ability of the network to classify the organization’s certification status by 94.00% according to the training sample and its ability to predict 91.00% of the test sample, with an overall prediction efficiency of 91.30%. This work provides insights and adds to the environmental performance evaluation literature providing a neural network model based on ISO 14031 guidelines that can be extended to include other international standards such as ISO 9001. This study supports the merging of ISO 14001 with ISO 14031 into a binding standard.

scholarly journals An Intelligent Approach to Recognizing Human Movements Based on a Wearable Device

2021 ◽  
Author(s):  
Anastasya Grecheneva ◽  
Nikolay Dorofeev ◽  
Maxim Goryachev
Keyword(s):  
Neural Network ◽  
Basic Structure ◽  
Activation Function ◽  
Training Sample ◽  
Wearable Device ◽  
Output Layer ◽  
The Neural Network ◽  
Human Movements ◽  
Hidden Layer ◽  
Propagation Network

n this paper, we consider the possibility of distinguishing the movements of a person and people by their gait based on data obtained from the accelerometer of a wearable device. A mobile phone was used as a wearable device. The paper considers the features of recognizing human movements based on a wearable device. A recognition algorithm based on a neural network with preliminary data processing and correlation analysis is proposed. The volume of the training sample consisted of 32 subjects with various physiological characteristics. The sample size in the subgroup of four people ranged from 2000 to 3000 movements. The main motor patterns for classification were the movements performed when walking in a straight line and stairs with a load (a bag with a laptop weighing 3.5 kg) and without it. The direct propagation network is chosen as the basic structure for the neural network. The neural network has 260 input neurons, 100 neurons in one hidden layer, and 4 neurons in the output layer. When training the neural network, the gradient reverse descent function was used. Cross- entropy was used as an optimization criterion. The activation function of the hidden layer was a sigmoid, and the output layer was a normalized exponential function. The presented algorithm makes it possible to distinguish between subjects when performing different movements in more than 90% of cases. The practical application of the results of the work is relevant for automated information systems of the medical, law enforcement and banking sectors.

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