Hearing Loss Detection in Medical Multimedia Data by Discrete Wavelet Packet Entropy and Single-Hidden Layer Neural Network Trained by Adaptive Learning-Rate Back Propagation

2017 ◽  
pp. 541-549 ◽  
Author(s):  
Shuihua Wang ◽  
Sidan Du ◽  
Yang Li ◽  
Huimin Lu ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Hearing Loss ◽  
Adaptive Learning ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Learning Rate ◽  
Multimedia Data ◽  
Discrete Wavelet ◽  
Hidden Layer ◽  
Medical Multimedia

scholarly journals Effect of Learning Rate on the Recognition of Images

1996 ◽  
Vol 19 (1) ◽  
pp. 1-12 ◽  
Author(s):  
M. Hamed ◽  
A. El Desouky
Keyword(s):  
Neural Network ◽  
Texture Classification ◽  
Back Propagation ◽  
Learning Rate ◽  
Computational Time ◽  
Rate Parameter ◽  
Modern Computer ◽  
The Neural Network ◽  
Hidden Layer ◽  
Propagation Technique

This paper presents a study for the effect of learning rate on an approach for texture classification and detection based on the neural network principle. This neural network consists of three layers, which are input, output, and hidden layers. The back propagation technique is considered. A computer algorithm is deduced and applied. In this work, the synthetic textures are generated. The results are taken for the modern computer of AT 486 type. The mathematical analysis is summarized in order to illustrate the effect of learning rate parameter on the exact discrimination during processing. This effect is studied through applications. The minimum consumed time for the computational time of classification in industry is correlated to correspond only the use of only 2 units in the hidden layer of a neural network for real images instead of 11 units.

SOLVING LOCAL MINIMA PROBLEM IN BACK PROPAGATION ALGORITHM USING ADAPTIVE GAIN, ADAPTIVE MOMENTUM AND ADAPTIVE LEARNING RATE ON CLASSIFICATION PROBLEMS

2012 ◽  
Vol 09 ◽  
pp. 448-455 ◽  
Author(s):  
NORHAMREEZA ABDUL HAMID ◽  
NAZRI MOHD NAWI ◽  
ROZAIDA GHAZALI ◽  
MOHD NAJIB MOHD SALLEH
Keyword(s):  
Adaptive Learning ◽  
Gradient Descent ◽  
Back Propagation ◽  
Learning Rate ◽  
Benchmark Problems ◽  
Local Minima ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Hidden Layer ◽  
Adaptive Momentum

This paper presents a new method to improve back propagation algorithm from getting stuck with local minima problem and slow convergence speeds which caused by neuron saturation in the hidden layer. In this proposed algorithm, each training pattern has its own activation functions of neurons in the hidden layer that are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. The efficiency of the proposed algorithm is compared with the conventional back propagation gradient descent and the current working back propagation gradient descent with adaptive gain by means of simulation on three benchmark problems namely iris, glass and thyroid.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

scholarly journals Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

BioResources ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 2369-2384
Author(s):  
Weihang Dong ◽  
Xiaolei Guo ◽  
Yong Hu ◽  
Jinxin Wang ◽  
Guangjun Tian
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Monitoring Model ◽  
Plastic Composite ◽  
Spindle Power

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

2022 ◽  
pp. 1301-1312
Author(s):  
M. Outanoute ◽  
A. Lachhab ◽  
A. Selmani ◽  
H. Oubehar ◽  
A. Snoussi ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Relative Humidity ◽  
Back Propagation ◽  
Particle Swarm ◽  
Simulated Data ◽  
Swarm Optimization ◽  
Generalization Ability ◽  
Optimal Weights ◽  
Hidden Layer

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

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