mlp neural network
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MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 537-542
Author(s):  
GIRISH K. JHA ◽  
GAJAB SINGH ◽  
S. VENNILA ◽  
M. HEGDE ◽  
M. S. RAO ◽  
...  

A multi-layer perceptron (MLP) neural network model for predicting adult moth population of tobacco caterpillar (Spodoptera litura (Fabricius) in groundnut cropping system of Dharwad (Karnataka) was developed and tested using the long term (24 years : 1990-2013) trap catches of the pest and weather data of Kharif season [26 to 44 standard meteorological weeks (SMW)]. The weekly male moth catches of S. litura during maximum severity observed at 34 SMW was modelled using the weather parameters viz., maximum temperature (C), minimum temperature (°C), rainfall (mm) and morning and afternoon relative humidity (%) lagged by two weeks. The principle component analysis was performed using meteorological data of preceding two weeks (32 and 33 SMW) in order to create fewer linearly independent factors. Five principal component scores which together accounted for 90 per cent of variations in data were used as input variables for neural network model. A MLP neural network with five input nodes and one hidden layer consisting of eleven hidden nodes was found to be suitable in terms of adequacy measures for modelling the population dynamics of S. litura. While data sets of 1990-2009 were used for developing the model, data of four seasons (2010-2013) were used for testing and validation. The performance of the model was assessed in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The validation results clearly showed that the neural network based model is effective in dealing with the apparently random behaviour of the S. litura dynamics on groundnut.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenda Wei ◽  
Chengxia Liu ◽  
Jianing Wang

PurposeNowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.Design/methodology/approachFirstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.FindingsResults show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.Originality/valueCompared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.


Author(s):  
Yajun Liu ◽  
Shenchao Zhang ◽  
Zhendong Liu

In practice, the volatile organic compounds (VOCs) pollution can exist when refueling due to the properties of the gasoline, low viscosity and high saturated-vapor pressure. A new gasoline vapor recovery system involving frequency conversion technology and machine learning is developed to cope with this problem. In the proposed system, firstly, the pumping capacity of the vacuum pump is evaluated, and test shows an almost linear relationship between suction volume and frequency. Then, the Multi-Layer Perception (MLP) neural network and the support vector regression (SVR) are employed to predict the gas-liquid ratio, and the numerical examples are presented to prove the high prediction accuracy of the MLP and SVR, respectively, where the MLP neural network has better generalization ability. Finally, compared with the two gasoline vapor recovery systems based on the 1: 1 fixed control model and the PID control model, respectively, the gasoline vapor recovery efficiency is improved significantly by the new gasoline vapor recovery system.


2021 ◽  
Vol 14 (2) ◽  
pp. 28-34
Author(s):  
Sergey Pobeda ◽  
M. Chernyh ◽  
F. Makarenko ◽  
Konstantin Zolnikov

The article deals with the creation of a behavioral model of lateral metal oxide transistors (LDMOS) based on a neural network of the multilayer percep-tron type. The model is identified using a backpropa-gation algorithm. Demonstrated the process of creating an ANN model using Pytorch, a machine learning framework for the Python language, with subsequent transfer to the standard analog circuit modeling lan-guage Verilog-A.


2021 ◽  
Vol 24 (67) ◽  
pp. 147-156
Author(s):  
Amin Rezaeipanah ◽  
Neda Boroumand

Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.


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