scholarly journals Penggunaan Algoritma Jaringan Syaraf Tiruan Propagasi Balik Pada Klasifikasi Data Penggunaan Daya Listrik

2020 ◽  
Vol 5 (2) ◽  
pp. 1-6
Author(s):  
Zeni Permatasari ◽  
Agus Sifaunajah ◽  
Nur Khafidhoh
Keyword(s):  
Neural Network ◽  
Electrical Power ◽  
Back Propagation ◽  
Electrical Equipment ◽  
Training Data ◽  
Large Contribution ◽  
Data Set ◽  
Operational Costs ◽  
Network Algorithm ◽  
Neural Network Algorithm

Electrical Energy has a large contribution to the operational costs that must be incurred. The selection of electrical equipment can be one alternative that might be implemented to reduce operational costs incurred. In its use sometimes users do not know any electrical equipment that uses high electrical power and low electrical power. Therefore a system was made to classify data on electric power usage. This data will be classified into four classes, such as: very efficient, efficient, quite efficient and wasteful. Data classification is done using a back propagation neural network algorithm. The training data set used is 190 data and the test data set is 30 data. Based on the training that has been done, the optimal parameters are learning rate 0.5, target error 0.001, max epoch 10000, and 25 hidden neurons. Tests show that the system is able to recognize data with an accuracy level of 96.67% and MSE of 0.03333. Of the 30 data that have been tested obtained 29 data in accordance with the target. Where the 29 data are classified into 4 classes, namely 9 data classes are very efficient, 6 data classes are efficient, 5 data classes are quite efficient and 9 data classes are wasteful. The results of this study can be concluded that the backpropagation neural network algorithm can be implemented to classify electrical power usage data.

scholarly journals Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 61-64
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Probabilistic Neural Network ◽  
Media Analysis ◽  
Training Data ◽  
The Internet ◽  
Accuracy Score ◽  
Network Algorithm ◽  
The Social ◽  
Neural Network Algorithm

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

scholarly journals RESEARCH ON ATHLETES’ PSYCHOLOGICAL REGULATION ABILITY BASED ON BACK PROPAGATION (BP) NEURAL NETWORK ALGORITHM

2021 ◽  
Vol 27 (spe2) ◽  
pp. 83-86
Author(s):  
Yun Tan ◽  
Guoqing Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Psychological Control ◽  
Back Propagation ◽  
Rotation Matrix ◽  
Psychological State ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Game State

ABSTRACT Athletes’ psychological control ability directly affects competitions. Therefore, it is necessary to supervise the athletes’ game psychology. Athletes’ game state supervision model is constructed through the facial information extraction algorithm. The homography matrix and the calculation method are introduced. Then, two methods are introduced to solve the rotation matrix from the homography matrix. After the rotation matrix is solved, the method of obtaining the facial rotation angle from the rotation matrix is introduced. The two methods are compared in the simulation data, and the advantages and disadvantages of each algorithm are analyzed to determine the method used in this paper. The experimental results show that the model prediction accuracy reaches 70%, which can effectively supervise the psychological state of athletes. This research study is of great significance to improve the performance of athletes in competitions and improve the application of back propagation (BP) neural network algorithm.

scholarly journals NEURAL NET USING TO DETERMINE DEPTH AND FREQUENCY OF SIGNALS’ MODULATION FOR ELECTRICAL EQUIPMENT ULTRASONIC VIBROCONTROL

2021 ◽  
pp. 21-30
Author(s):  
Anatoly V. Bychkov ◽  
Irina Yu. Bychkova ◽  
Nadezhda N. Suslova ◽  
Kurbangali K. Alimov
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Vibration Control ◽  
Sampling Rate ◽  
Vibration Signal ◽  
Electrical Equipment ◽  
Neural Net ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Mean Square Errors

The apparatus of artificial neural networks (ANN) is proposed to be used for signal processing in active ultrasonic (US) vibration control of electrical equipment. A feature of the applied neural network algorithm is that the required information about vibration parameters is embedded in the ultrasound signal’s phase change at its constant amplitude. Under these conditions, traditional spectral analysis of signals requires a high sampling rate and a significant recording duration. When using the direct propagation’s ANN with three hidden layers, it was shown that it is sufficient to use a sampling frequency of 5-6 points for the period of an ultrasonic wave and a recording duration of 4-5 periods to estimate the nonstationary frequency and amplitude of the vibration signal. Estimates of the error in determining the amplitude, frequency and phase of vibrations are obtained. The root-mean-square errors of the neural network algorithm do not exceed units of percent. The possibilities of using a trained neural network for signal processing in a «sliding window» are demonstrated. The accuracy characteristics of the proposed neural network algorithm of signal processing and the possibility of its optimization for electrical equipment’s vibration control are discussed.

scholarly journals Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

2021 ◽  
pp. 47-57
Author(s):  
Diyar Waysi Naaman
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Back Propagation ◽  
Image Transmission ◽  
Search Space ◽  
Compression Technique ◽  
Advanced Technique ◽  
Fractal Image ◽  
Network Algorithm ◽  
Neural Network Algorithm

Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

Research and Realization of Hardware Back-Propagation Neural Network Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 2469-2474
Author(s):  
Fei Guo ◽  
Xiao Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Variable Structure ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
Neural Network Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Timing Simulation

In order to meet the requirements of real-time and embedded of industrial field, a reconfigurable Back-Propagation neural network based on FPGA has been implemented on Xilinx's Spartan-3E (XC3S250E) chip which has 250000 gate. First the optimal network structure and weights were gotten by a variable structure of BP neural network algorithm. Then an improved hardware approaching method of excitation function was put forward, and the maximum error was 1.58% by simulation and comparative analysis on the error. Finally hardware co-imitation and timing simulation was token based on a reasonable choice of data accuracy, and then the hardware BP neural network algorithm was been downloaded and implemented on FPGA. This method has better accuracy and speed, it is an effective method of BP neural network modeling based on hardware, and lays the foundation for the hardware realization of other neural network and embedded image processing.

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