scholarly journals Hybrid Element Heuristic Algorithm Optimizing Neural Network-Based Educational Courses

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chunlei Tu ◽  
Yanjin Liu ◽  
Lixiao Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Educational Technology ◽  
Design And Development ◽  
Learning Platform ◽  
The Neural Network ◽  
Starting Point ◽  
Annealing Algorithm ◽  
Level Of Understanding ◽  
Educational Technology Courses

The rapid development of computer networks has enabled information technology to penetrate many fields, providing unprecedented opportunities for all aspects of our lives. In order to allow students to acquire necessary knowledge and skills through efficient learning, this article studies the design and development of educational technology courses based on hybrid metaheuristic algorithms to optimize neural networks. This paper proposes a metaheuristic algorithm and explains the simulated annealing algorithm and microregular annealing algorithm in detail. Using these algorithms, a mathematical model of the normal scheduling problem was also constructed, and the mathematical model was applied to the design and development of educational technology courses. In addition, the neuron model in the neural network and the activation function of the neural network are discussed from various aspects. In the experiment, according to the needs of students, a learning platform for educational technology courses was designed and developed. The experimental results of this article show that there are significant differences in the starting point of the ability level of learners for different majors. Education majors have a higher level of understanding of educational technology courses; 38.20% of students know well, while art majors have a low level of understanding of education technology courses, with 36.05% of students majoring in art.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

scholarly journals Using high-performance deep learning platform to accelerate object detection

2019 ◽  
pp. 354-360 ◽  
Author(s):  
S O Stepanenko ◽  
P Y Yakimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing Speed ◽  
High Performance ◽  
Object Classification ◽  
High Accuracy ◽  
Learning Platform ◽  
The Neural Network

Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.

scholarly journals Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder

2021 ◽  
Author(s):  
Matthew S. Willsey ◽  
Samuel R. Nason ◽  
Scott R. Ensel ◽  
Hisham Temmar ◽  
Matthew J. Mender ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Finger Movements ◽  
Feed Forward Neural Network ◽  
Current Standard ◽  
Biologically Inspired ◽  
Current State ◽  
The Neural Network ◽  
Starting Point ◽  
Prosthetic Finger

AbstractDespite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network, loosely inspired by the biological neural pathway, to decode real-time two-degree-of-freedom finger movements. Using a two-step training method, a recalibrated feedback intention–trained (ReFIT) neural network achieved a higher throughput with higher finger velocities and more natural appearing finger movements than the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein are the first to demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.

scholarly journals METHOD OF CONSTRUCTION OF A NEUROREGULATOR MODEL WHEN OPTIMIZING THE CONTROL STRUCTURE OF A TECHNOLOGICAL CYCLE

Doklady BGUIR ◽  
2019 ◽  
pp. 125-132
Author(s):  
V. S. Smorodin ◽  
V. A. Prokhorenko
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Phase Plane ◽  
Control Structure ◽  
Movement Direction ◽  
Technological Cycle ◽  
The Neural Network ◽  
System States ◽  
The Mathematical Model ◽  
The Given

In this paper authors present the results of a research that had a purpose to develop a method of constructing a neuroregulator model for the case of optimization of the control structure of a technological cycle. The method's implementation is based upon the automation of a production process when a physical controller, that operates the technological process according to a given program, is present. In order to achieve this goal, the artificial neural network approaches were implemented to create a mathematical model of the neuroregulator. The mathematical model of the neuroregulator is based on a physical prototype, and the procedure of a real-time control synthesis (adaptive control) is based on recurrent neural network training. The neural network architecture includes LSTM blocks, which are capable of storing information for long periods of time. A method is proposed for constructing a neuroregulator model for control of a production cycle when solving the task of the optimal trajectory finding on the phase plane of the technological cycle states. In the considered task of the optimal trajectory finding the mathematical model of the neuroregulator receives at each moment of time information about the current system state, the adjacent system states and the movement direction on the phase plane of states. Movement direction is determined by the given control optimization criteria. Based on the research results it was found that recurrent networks with LSTM modules can be used successfully as an approximator for the agent's Q-function to solve the given problem when the partially observed region of system states has a complex structure. The choice of the method of adaptation to the control actions and the external environmental disturbances proposed in the paper satisfies the requirements for the adatation process performance, as well as the requierments for the control processes quality, when there is lack of information about the nature of random control disturbances. The experimental environment, as well as the neural network models was implemented using the Python programming language with TensorFlow library.

Application of a Feedforward Neural Network to Predicting the Thermal Characteristics of Machine Tools

2021 ◽  
Vol 27 (4) ◽  
pp. 202-211
Author(s):  
A. N. Polyakov ◽  
◽  
V. V. Pozevalkin ◽  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Machine Tools ◽  
Thermal Characteristics ◽  
Software Tool ◽  
Neural Network Modeling ◽  
Machine Experiment ◽  
The Neural Network ◽  
Artificial Neural

he paper presents a procedure for studying the stability of modeling an artificial neural network as applied to the thermal characteristics of machine tools. The topicality of this procedure is dictated by the ambiguity of the results generated by the neural network when constructing the predicted thermal characteristics of machine tools. Therefore, to select one of the possible solutions generated by the neural network, it was proposed to use two criteria. The effectiveness of their use is confirmed by the presented machine experiments. The methodology proposed in this work has made it possible to form a generalized concept for studying the effectiveness of the use of neural network technologies in thermal modeling of machine tools. This concept defines a typical set of variable modeling parameters, a basic mathematical model based on a modal approach, and an architecture of a typical software tool that can be developed to study the effectiveness of artificial neural network modeling. For each variant of the input data of the network, the following parameters were varied: the number of neurons in the hidden layer; the size of the input and output vectors; input vectors error; the size of the training, validation and test sample; functional features of thermal characteristics supplied to the network input or their multimodality; the presence and absence of normalization of the input vector. The paper presents experimental thermal characteristics for two spindle speeds of a vertical CNC machine. The results of the machine experiment are presented for six variants of the variable parameters of the mathematical model. The software tool used to carry out the machine experiment was developed in Matlab.

3rd Degree Mathematical Model Appropriate for Parametric Estimation of SAW Process

2011 ◽  
Vol 284-286 ◽  
pp. 2473-2476
Author(s):  
Aniruddha Ghosh ◽  
N.K. Singh ◽  
Somnath Chattopadhyaya
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Arc Welding ◽  
Travel Speed ◽  
Parametric Estimation ◽  
Bead Width ◽  
The Neural Network ◽  
Current Voltage ◽  
Submerged Arc ◽  
Made In

An attempt has been made in this paper to develop a appropriate model for predicting the output responses of Submerged Arc Welding (SAW) process with the help of neural network technique. Also a mathematical model has been developed to study the effects of input variable (i.e. current, voltage, travel speed) on output responses (i.e. reinforcement height, weld bead width, metal deposition rate). SAW process has been chosen for this application because of the complex set of variables involved in the process as well as its significant application in the manufacturing of critical equipments which have a lot of economic and social implications. Under this study the neural network model is trained according to the actual inputs and outputs. When the training is completed then the desired inputs are given to the model and it gives the estimated output value. And according to this we can also estimate the error between the actual and predicted results. Side by side accurecy of mathematical model has been checked.

Study and Implementation of Expendable Current Profiler Measurement

2011 ◽  
Vol 301-303 ◽  
pp. 133-138
Author(s):  
Guo Xing Ren ◽  
Yan Cheng
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Mathematical Model ◽  
Work Environment ◽  
Network Model ◽  
Research Direction ◽  
Future Research ◽  
Future Research Direction ◽  
The Neural Network ◽  
Current Profiler

This paper conducts research on expendable current profiler measurement. According to the principle of expendable current profiler measurement, this paper builds a mathematical model and discusses the composition of the system as well as the design of the sensor electrode on this basis. At the same time, against its complex work environment, it provides the neural network modification algorithm with the generalization ability, constructs the network model and carries out data modification test using this algorithm, finally it discusses part of the experimental data of the system. Through the analysis of the experimental results, it elaborates the advantages of the system and indicates the future research direction.

Modelling of a Novel Gas Strut Using Neural Networks

2004 ◽  
Author(s):  
B. Gao ◽  
J. Darling ◽  
D. G. Tilley ◽  
R. A. Williams ◽  
A. Bean ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Least Square ◽  
Operation Conditions ◽  
The Neural Network ◽  
Wide Range ◽  
Non Linear ◽  
Vehicle Suspension System ◽  
And Control

The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal ‘black-box’ models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.

Sign in / Sign up

Export Citation Format

Share Document