scholarly journals Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shoujing Zhang ◽  
Xiaofan Qin ◽  
Sheng Hu ◽  
Qing Zhang ◽  
Bochao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Inventory Management ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Spare Part ◽  
Back Propagation Neural Network ◽  
Spare Parts ◽  
Training Data ◽  
Importance Value ◽  
The Impact

The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management. Most of the methods used in previous research are subjective. For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper. First, we classified the spare parts by analyzing their historical maintenance and inventory data. Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data. Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts. The previous importance evaluation methods were susceptible to subjective factors during the evaluation process. The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data. The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation. At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

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 Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network

2018 ◽  
Vol 4 (2) ◽  
pp. 90-99
Author(s):  
Mertha Endah Ervina ◽  
Rini Silvi ◽  
Intaniah Ratna Nur Wisisono
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Percentage Error ◽  
Network Architectures ◽  
Forecasting Accuracy ◽  
Resilient Back Propagation ◽  
Very High

Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.

CLASSIFICATION OF sEMG SIGNALS USING INTEGRATED NEURAL NETWORK WITH SMALL SIZED TRAINING DATA

2012 ◽  
Vol 24 (04) ◽  
pp. 365-376 ◽  
Author(s):  
Baofeng Sun ◽  
Wanzhong Chen
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Accurate Method ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Semg Signal ◽  
Elman Neural Network ◽  
Robotic Devices ◽  
Semg Signals

The sEMG (Surface electromyography) signals detected from activated muscles can be used as a control source for prosthesis. So an efficient and accurate method for the classification of sEMG signal patterns has become a hot research in recent years. Artificial neural network is a popular used method in this field, however, most neural networks require large numbers of samples in the training stage to obtain the potential relationships between input feature vectors and the outputs. In this paper, Integrated back propagation neural network (IBPNN) is used to classify sEMG signals acquired during five different hand motions. The correct classification rates of IBPNN for the five hand movements are significantly higher than that of BPNN and Elman neural network. This reveals that IBPNN achieves the best performance with a small sized training data and can be used in control systems on prosthetic hands and other robotic devices based on electromyography pattern recognition.

System Identification of UAV Alap-Alap Using Back Propagation Neural Network

2013 ◽  
Vol 373-375 ◽  
pp. 1212-1219
Author(s):  
Afrias Sarotama ◽  
Benyamin Kusumoputro
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Data Sets ◽  
Input Output ◽  
Data Set ◽  
Flight Data ◽  
The Neural Network ◽  
Aerial Vehicle

A good model is necessary in order to design a controller of a system off-line. It is especially beneficial in the implementation of new advanced control schemes in Unmanned Aerial Vehicle (UAV). Considering the safety and benefit of an off-line tuning of the UAV controllers, this paper identifies a dynamic MIMO UAV nonlinear system which is derived based on the collection of input-output data taken from a test flights (36250 samples data). These input-output sample flight data are grouped into two flight data sets. The first flight data set, a chirp signal, is used for training the neural network in order to determine parameters (weights) for the network. Validation of the network is performed using the second data set, which is not used for training, and is a representation of UAV circular flight movement. An artificial neural network is trained using the training data set and thereafter the network is excited by the second set input data set. The predicted outputs based on our proposed Neural Network model is similar to the desired outputs (roll, pitch and yaw) which has been produced by real UAV system.

scholarly journals Identification of Anesthesia Stages from EEG Signals using Wavelet Entropy and Backpropagation Neural Network

2019 ◽  
pp. 17-20
Author(s):  
Ahmed Abdal Shafi Rasel
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Eeg Signals ◽  
The Neural Network ◽  
Power Spectral ◽  
Eeg Data ◽  
Density Analysis ◽  
Key Features

This study focuses on entropy based analysis of EEG signals for extracting features for a neural network based solution for identifying anesthetic levels. The process involves an optimized back propagation neural network with a supervised learning method. We provided the extracted features from EEG signals as training data for the neural network. The target outputs provided are levels of anesthesia stages. Wavelet analysis provides more effective extraction of key features from EEG data than power spectral density analysis using Fourier transform. The key features are used to train the Back Propagation Neural Network (BPNN) for pattern classification network. The final result shows that entropy-based feature extraction is an effective procedure for classifying EEG data.

scholarly journals High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2324 ◽  
Author(s):  
Haiqi Zhang ◽  
Jiahe Cui ◽  
Lihui Feng ◽  
Aiying Yang ◽  
Huichao Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
High Precision ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Set ◽  
Positioning Accuracy ◽  
Localization Accuracy

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

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