scholarly journals Knowledge Extraction from Trained Neural Network Scour Models

2008 ◽  
Vol 2 (4) ◽  
Author(s):  
H. Md. Azamathulla ◽  
Aminuddin Ab Ghani ◽  
Nor Azazi Zakaria ◽  
Chang Chun Kiat ◽  
Leow Cheng Siang
Keyword(s):  
Neural Network ◽  
Knowledge Extraction ◽  
Trained Neural Network

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

Study on Neural Networks Usage to Analyse Correlation between Spectrum of Vibration Acceleration Signal from Pin of Ball Mill and its Filling Level

2015 ◽  
Vol 770 ◽  
pp. 540-546 ◽  
Author(s):  
Yuri Eremenko ◽  
Dmitry Poleshchenko ◽  
Anton Glushchenko
Keyword(s):  
Neural Network ◽  
Ball Mill ◽  
Signal Amplitude ◽  
Signal Spectrum ◽  
Laboratory Model ◽  
Training Set ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
The Neural Network ◽  
Trained Neural Network

The question about modern intelligent information processing methods usage for a ball mill filling level evaluation is considered. Vibration acceleration signal has been measured on a mill laboratory model for that purpose. It is made with accelerometer attached to a mill pin. The conclusion is made that mill filling level can not be measured with the help of such signal amplitude only. So this signal spectrum processed by a neural network is used. A training set for the neural network is formed with the help of spectral analysis methods. Trained neural network is able to find the correlation between mill pin vibration acceleration signal and mill filling level. Test set is formed from the data which is not included into the training set. This set is used in order to evaluate the network ability to evaluate the mill filling degree. The neural network guarantees no more than 7% error in the evaluation of mill filling level.

scholarly journals Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ravi Kiran ◽  
Dayakar L. Naik
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Analytical Quality ◽  
Feed Forward Neural Network ◽  
First Derivative ◽  
Network Output ◽  
Novel Method ◽  
Classification Tasks ◽  
Trained Neural Network ◽  
Sensitivity Method

AbstractEvaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified.

scholarly journals Nonlinear Aeroelastic System Identification Based on Neural Network

2018 ◽  
Vol 8 (10) ◽  
pp. 1916
Author(s):  
Bo Zhang ◽  
Jinglong Han ◽  
Haiwei Yun ◽  
Xiaomao Chen
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Control Surface ◽  
Identification Algorithm ◽  
Aeroelastic System ◽  
Structural Nonlinearity ◽  
Time Marching ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

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