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.

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.

Mathematical model of the artificial muscle with two actuators

2020 ◽  
pp. 095440622094156
Author(s):  
Ljubinko B Kevac ◽  
Mirjana M Filipovic ◽  
Ana M Djuric
Keyword(s):  
Mathematical Model ◽  
Control Structure ◽  
Artificial Muscle ◽  
Parallel Robot ◽  
Simulation Results ◽  
Proportional Derivative Controller ◽  
Definition Of ◽  
The Mathematical Model ◽  
The Given ◽  
Very High

Characteristic construction of cable-suspended parallel robot of artificial muscle, which presents an artificial forearm, is analyzed and synthesized. Novel results were achieved and presented. Results presented in this paper were initially driven to recognize and mathematically define undefined geometric relations of the artificial forearm since it was found that they strongly affect the dynamic response of this system. It gets more complicated when one has more complex system, which uses more artificial muscle subsystems, since these subsystems couple and system becomes more unstable. Unmodeled or insufficiently modeled dynamics can strongly affect the system’s instability. Because of that, the construction of this system and its new mathematical model are defined and presented in this paper. Generally, it can be said that the analysis of geometry of selected mechanism is the first step and very important step to establish the structural stability of these systems. This system is driven with two actuators, which need to work in a coordinated fashion. The aim of this paper is to show the importance of the geometry of this solution, which then strongly affects the system’s kinematics and dynamics. To determine the complexity of this system, it was presumed that system has rigid cables. Idea is to show the importance of good defined geometry of the system, which gives good basis for the definition of mathematical model of the system. Novel program package AMCO, artificial muscle contribution, was defined for the validation of the mathematical model of the system and for choice of its parameters. Sensitivity of the system to certain parameters is very high and hence analysis of this system needs to be done with a lot of caution. Some parameters are very influential on the possible implementation of the given task of the system. Only after choosing the parameters and checking the system through certain simulation results, control structure can be defined. In this paper, proportional–derivative controller was chosen.

scholarly journals A System for Detecting Anomalous Observations in Data on Healthcare Services

2021 ◽  
Vol 27 (3) ◽  
pp. 356-367
Author(s):  
S. L. Blyumin ◽  
R. V. Scheglevatykh ◽  
A. A. Naydenov ◽  
A. S. Sysoev
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Healthcare Services ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Incoming Information ◽  
Process Behavior ◽  
The Mathematical Model

A description of the mathematical model of a neural network classifier of data on healthcare in the institutions of the Lipetsk region is given in order to identify atypical (abnormal) records. Anomaly detection refers to the problem of finding data that is inconsistent with some expected process behavior or metric occurring in the system. Due to the large number of inputs to the neural network model, the time it takes to process the incoming information also increases. To assess what factors should be transmitted to the input of the neural network classifier, an approach to the reduction of the neural network model based on sensitivity analysis is proposed. The description of a set of software tools for solving the problem is presented.

Scheduling Problem of Biochemical Analyzer and Exploration of Neural Network-Greedy Algorithm

2020 ◽  
Vol 10 (8) ◽  
pp. 1912-1918
Author(s):  
Xiaohui Huang ◽  
Shuxia Zheng ◽  
Shilong Li ◽  
Jinxiang Wu ◽  
Graham Spence
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Greedy Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Scheduling Problem ◽  
The Neural Network ◽  
Biochemical Analyzer ◽  
Analysis System ◽  
The Mathematical Model

The mathematical model of biochemical analysis system was established based on neural network-greedy algorithm. The optimal task scheduling sequence was solved by neural network algorithm. At the same time, the local optimization was obtained by combining greedy algorithm. In this way, the task scheduling problem in biochemical analyzer was transformed into a mathematical problem, and the mathematical model of scheduling algorithm was established. On the platform of MATLAB, eight groups of simulation tests were carried out on the same task scheduling problem using the neural network-greedy scheduling algorithm and the traditional fixedperiod scheduling algorithm. The task-time Gantt charts of the two algorithms were compared under different scheduling orders. The results showed that the average speed of the neural network-greedy algorithm was improved by 31% compared with that of the fixed-period scheduling algorithm. The mathematical model of biochemical analysis system on scheduling problem established by neural network-greedy scheduling algorithm has high efficiency compared with the traditional fixed-period scheduling algorithm.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

scholarly journals Mathematical modeling of physical processes in the complex for testing of induction machines

2018 ◽  
Vol 239 ◽  
pp. 01055 ◽  
Author(s):  
Viktor Kharlamov ◽  
Denis Popov
Keyword(s):  
Mathematical Model ◽  
Frequency Converter ◽  
Electrical Energy ◽  
Induction Machines ◽  
Test Scheme ◽  
Load Testing ◽  
Variable Parameters ◽  
Asynchronous Machines ◽  
The Mathematical Model ◽  
The Given

The paper is devoted to the simulation of the test complex designed for energy-efficient load testing of induction machines by the method of mutual load with the exchange of electrical energy through the network. It is noted that for other similar test schemes, the mathematical model will have a slightly different form, but it will be identical in terms of asynchronous machines, network and frequency converter. The compiled mathematical model of the test complex allows studying the variable parameters of the system in all elements of the test scheme in static and dynamic modes of operation as well. The synthesized mathematical model can be used to determine the parameters of the equipment in the designed test complexes if the parameters of the test and load machines are known. The results of simulation of the test complex for the given parameters of the test and load induction machines are obtained.

scholarly journals Design Space Exploration for YOLO Neural Network Accelerator

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1921
Author(s):  
Hongmin Huang ◽  
Zihao Liu ◽  
Taosheng Chen ◽  
Xianghong Hu ◽  
Qiming Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Computation Time ◽  
Memory Bandwidth ◽  
Block Transmission ◽  
Transmission Strategy ◽  
The Neural Network ◽  
The Given

The You Only Look Once (YOLO) neural network has great advantages and extensive applications in computer vision. The convolutional layers are the most important part of the neural network and take up most of the computation time. Improving the efficiency of the convolution operations can greatly increase the speed of the neural network. Field programmable gate arrays (FPGAs) have been widely used in accelerators for convolutional neural networks (CNNs) thanks to their configurability and parallel computing. This paper proposes a design space exploration for the YOLO neural network based on FPGA. A data block transmission strategy is proposed and a multiply and accumulate (MAC) design, which consists of two 14 × 14 processing element (PE) matrices, is designed. The PE matrices are configurable for different CNNs according to the given required functions. In order to take full advantage of the limited logical resources and the memory bandwidth on the given FPGA device and to simultaneously achieve the best performance, an improved roofline model is used to evaluate the hardware design to balance the computing throughput and the memory bandwidth requirement. The accelerator achieves 41.99 giga operations per second (GOPS) and consumes 7.50 W running at the frequency of 100 MHz on the Xilinx ZC706 board.

scholarly journals Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 571 ◽  
Author(s):  
Mateusz Dybkowski ◽  
Kamil Klimkowski
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Control Algorithm ◽  
Control Structure ◽  
Fault Tolerant ◽  
Current Sensor ◽  
Sensor Faults ◽  
Safety Level ◽  
The Neural Network ◽  
Network Application

This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. For the proper operation of this system and for other vector algorithms, information about the stator currents components is required. It is important to monitor and detect these sensor faults, especially in drives with an increased safety level. We discuss the possibility of the neural network application in detecting stator current sensor faults in the vector control algorithm. Simulation and experimental results for various drive conditions are presented.

The Application of Double Closed-Loop Unit Gain Negative Feedback and BP Neural Network Controller in Single Inverted Pendulum

2012 ◽  
Vol 490-495 ◽  
pp. 1723-1727
Author(s):  
Jun Ting Wang ◽  
Guo Ping Liu ◽  
Wei Jin ◽  
Gen Fu Xiao
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Negative Feedback ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Inverted Pendulum ◽  
Root Locus ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Mathematical Model

In the paper the mathematical model of the single inverted pendulum is established, on the base of the root locus and the control tasks the control system is made up of double closed-loop unit gain negative feedback and BP neural network controller. The results show that the inverted pendulum is efficiently controlled.

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