scholarly journals ARCHITECTURE AND TRAINING ALGORITHM FOR NEURAL NETWORK TO RECOGNIZE VOICE SIGNALS

2020 ◽  
Vol 0 (3) ◽  
pp. 99-107
Author(s):  
V. S. Molchanova ◽  
D. S. Mironenko
Keyword(s):  
Neural Network ◽  
Training Algorithm ◽  
And Training

scholarly journals Solving Mixed Volterra - Fredholm Integral Equation (MVFIE) by Designing Neural Network

2019 ◽  
Vol 16 (1) ◽  
pp. 0116
Author(s):  
Al-Saif Et al.
Keyword(s):  
Neural Network ◽  
Integral Equation ◽  
Analytic Solution ◽  
Fredholm Integral Equations ◽  
Training Algorithm ◽  
Output Unit ◽  
Feed Forward Neural Network ◽  
Levenberg Marquardt ◽  
Hidden Layer ◽  
And Training

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.                                  

Research on Hyperspectral Data Classification Based on Quantum Counter Propagation Neural Network

2012 ◽  
Vol 546-547 ◽  
pp. 1377-1381
Author(s):  
Xuan Hou
Keyword(s):  
Neural Network ◽  
Quantum Theory ◽  
Topological Structure ◽  
Learning Algorithm ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Training Algorithm ◽  
Training Process ◽  
Traditional Classification ◽  
And Training

It proposes the model and learning algorithm of Quantum Counter Propagation Neural Network and applies which in hyperspectral data classification as well. On one hand, introducing quantum theory into the structure or training process of Counter Propagation Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Counter Propagation Neural Network by the means of quoting the thought, concept and principles of quantum theory directly. To complete the experiment of hyperspectral data classification with three ways and the result shows that effects of Quantum Counter Propagation Neural Network is superior to the traditional classification.

scholarly journals Architecture and Training Algorithm of The Adjusting Neural Network for Accurate Model Tuning.

1995 ◽  
Vol 115 (4) ◽  
pp. 461-469 ◽  
Author(s):  
Yoichi Sugita ◽  
Masahiro Kayama ◽  
Yasuo Morooka ◽  
Yutaka Saito
Keyword(s):  
Neural Network ◽  
Training Algorithm ◽  
Accurate Model ◽  
Model Tuning ◽  
And Training

Development of a Prediction Algorithm for Column Shortening in High-Rise Buildings Using a Neural Network

2007 ◽  
Vol 348-349 ◽  
pp. 901-904
Author(s):  
Won Jik Yang ◽  
Waon Ho Yi
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Prediction Algorithm ◽  
Training Methods ◽  
Training Algorithm ◽  
High Rise ◽  
Concrete Members ◽  
Polynomial Fit ◽  
Neural Network Algorithm ◽  
And Training

The objective of this study is to formulate and evaluate a new training algorithm of Neural Network to predict the inelastic shortening of reinforced concrete members using the column shortening data of high-rise buildings. The new training algorithm of Neural Network for the prediction of column shortening focuses on component of input data and training methods. The validity is examined by training and prediction process based on column shortening measuring data of high-rise buildings. The polynomial fit line of measuring data is used as the training data instead of measuring data. The result shows that the new Neural Network algorithm proposed in this study successfully predicts column shortening of high-rise buildings.

A Novel Neural Network Based Modeling for Control of NOx Emission in Power Plant

2014 ◽  
Vol 643 ◽  
pp. 385-390
Author(s):  
Ping Kang Li ◽  
Rui Pan ◽  
Chen Chen
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Model Identification ◽  
Training Algorithm ◽  
Optimal Prediction ◽  
Prediction Formula ◽  
Steady State Model ◽  
Identification Technique ◽  
One Step ◽  
And Training

A novel neural network based modeling for non-linear model identification technique is proposed. It combines a nonlinear steady state model with a linear one, to describe the disturbance and dynamics in the coal-fired power plant. The modeling and training algorithm is used to develop a model of nitrogen oxides (NOx) emitted from the process where one-step ahead optimal prediction formula are developed. Two cases show that the resulting model provides a better prediction of NOx and fitting capabilities.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

2021 ◽  
pp. 1-15
Author(s):  
Qinyu Mei ◽  
Ming Li
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control System ◽  
Convolutional Neural Network ◽  
Force Control ◽  
Control Experiment ◽  
Deep Convolutional Neural Network ◽  
Flexible Cable ◽  
Cable Force ◽  
And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

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