A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis

2013 ◽  
Vol 40 (8) ◽  
pp. 2941-2946 ◽  
Author(s):  
Sangjae Lee ◽  
Wu Sung Choi
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Prediction Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bankruptcy Prediction ◽  
Multivariate Discriminant Analysis ◽  
Multivariate Discriminant

scholarly journals IDENTIFIKASI SPESIES IKAN BERDASARKAN KONTUR OTOLITH MENGGUNAKAN METODE OTSU DAN BACK PROPAGATION NEURAL NETWORK

JOUTICA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 389
Author(s):  
Heri Susanto ◽  
Jamal Jamal
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Backpropagation Neural Network ◽  
Image Thresholding

Otolith merupakan organ yang sangat penting, karena melalui otolith dapat diketahui jenis ikan, pertumbuhan, lingkungan, serta sejarah kehidupannya, misalnya, umur, reproduksi, dan migrasi. Dengan semakin canggihnya komputer dan pengolahan di bidang citra, diharapkan kemampuan mengidenifikasi jenis ikan yang dimiliki oleh manusia bisa diadopsi dan diterapkan pada perangkat komputer. Threshold adalah sebuah teknik penting dalam aplikasi segmentasi citra. Hal mendasar dari threshold adalah menentukan nilai batas optimal dari citra keabuan, untuk memisahkan antara obyek dengan latar belakang. Metode Backpropagation Neural Network, merupakan metode klasifikasi yang handal untuk perhitungan yang rumit dengan waktu komputasi lebih sedikit, dan mampu memberikan nilai akurasi yang tinggi. Untuk keperluan segmentasi citra menggunakan metode Otsu karena metode ini merupakan metode paling berhasil untuk image thresholding. Proses klasifikasi untuk pengenalan spesies ikan berdasar otolith menggunakan metode Backpropagation Neural Network. Hasil eksperimen diperoleh akurasi sebesar 95% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.

Weight Loss Prediction Model for Pig Carcass Based on a Genetic Algorithm Back- Propagation Neural Network

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Loss ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Interval ◽  
Spraying Parameters ◽  
Pig Carcasses

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

scholarly journals Method of Predicting Ore Dilution Based on a Neural Network and Its Application

2020 ◽  
Vol 12 (4) ◽  
pp. 1550 ◽  
Author(s):  
Xingdong Zhao ◽  
Jia’an Niu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Deviation ◽  
Graph Method ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
The Difference

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

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