scholarly journals Neural-Network-Based Collaborative Control for Continuous Unknown Nonlinear Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Siyu Gao ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Cooperative Learning ◽  
Cooperative Control ◽  
Closed Loop ◽  
Collaborative Control ◽  
The Neural Network ◽  
Control Scheme ◽  
Optimal Value ◽  
Trained Neural Network

This paper proposes an NN-based cooperative control scheme for a type of continuous nonlinear system. The model studied in this paper is designed as an interconnection topology, and the main consideration is the connection mode of the undirected graph. In order to ensure the online sharing of learning knowledge, this paper proposes a novel weight update scheme. In the proposed update scheme, the weights of the neural network are discrete, and these discrete weights can gradually approach the optimal value through cooperative learning, thereby realizing the control of the unknown nonlinear system. Through the trained neural network, it is proved if the interconnection topology is undirected and connected, the state of the unknown nonlinear system can converge to the target trajectory after a finite time, and the error of the system can converge to a small neighbourhood around the origin. It is also guaranteed that all closed-loop signals in the system are bounded. A simulation example is provided to more intuitively prove the effectiveness of the proposed distributed cooperative learning control scheme at the end of the article.

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 Image Colorization

2019 ◽  
Author(s):  
Дарья Михалина ◽  
Daria Mikhalina ◽  
Александр Кузьменко ◽  
Aleksandr Kuz'menko ◽  
Константин Дергачев ◽  
...  
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Black And White ◽  
Number Of Layers ◽  
The Neural Network ◽  
Natural Landscapes ◽  
Video Card ◽  
Trained Neural Network ◽  
Lab Color Model ◽  
Cie Lab

The article discusses one of the latest ways to colorize a black and white image using deep learning methods. For colorization, a convolutional neural network with a large number of layers (Deep convolutional) is used, the architecture of which includes a ResNet model. This model was pre-trained on images of the ImageNet dataset. A neural network receives a black and white image and returns a colorized color. Since, due to the characteristics of ResNet, an input multiple of 255 is received, a program was written that, using frames, enlarges the image for the required size. During the operation of the neural network, the CIE Lab color model is used, which allows to separate the black and white component of the image from the color. For training the neural network, the Place 365 dataset was used, containing 365 different classes, such as animals, landscape elements, people, and so on. The training was carried out on the Nvidia GTX 1080 video card. The result was a trained neural network capable of colorizing images of any size and format. As example we had a speed of 0.08 seconds and an image of 256 by 256 pixels in size. In connection with the concept of the dataset used for training, the resulting model is focused on the recognition of natural landscapes and urban areas.

scholarly journals A Nonlinear System Identification Method Based on Adaptive Neural Network

2021 ◽  
Vol 28 (2) ◽  
pp. 111-123
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Generic Model ◽  
Control Engineering ◽  
The Neural Network ◽  
Adaptive Pso ◽  
And Control ◽  
Scientific Engineering

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

scholarly journals A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Wallace M. Bessa ◽  
Gerrit Brinkmann ◽  
Daniel A. Duecker ◽  
Edwin Kreuzer ◽  
Eugen Solowjow
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Weight Vector ◽  
Mechatronic Systems ◽  
Biologically Inspired ◽  
Control Approach ◽  
The Neural Network ◽  
Control Scheme ◽  
Intelligent Behavior ◽  
Intelligent Controllers

Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

scholarly journals Use of a Multilayer Perceptron to Automate Terrain Assessment for the Needs of the Armed Forces

2018 ◽  
Vol 7 (11) ◽  
pp. 430 ◽  
Author(s):  
Krzysztof Pokonieczny
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Learning Algorithm ◽  
Armed Forces ◽  
Shuttle Radar Topography Mission ◽  
Terrain Classification ◽  
The Neural Network ◽  
Data Configuration ◽  
Trained Neural Network

The classification of terrain in terms of passability plays a significant role in the process of military terrain assessment. It involves classifying selected terrain to specific classes (GO, SLOW-GO, NO-GO). In this article, the problem of terrain classification to the respective category of passability was solved by applying artificial neural networks (multilayer perceptron) to generate a continuous Index of Passability (IOP). The neural networks defined this factor for primary fields in two sizes (1000 × 1000 m and 100 × 100 m) based on the land cover elements obtained from Vector Smart Map (VMap) Level 2 and Shuttle Radar Topography Mission (SRTM). The work used a feedforward neural network consisting of three layers. The paper presents a comprehensive analysis of the reliability of the neural network parameters, taking into account the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions.

scholarly journals Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1662
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Prediction Method ◽  
High Speed Train ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

scholarly journals Modified Depth-Map Inpainting Method Using the Neural Network

2019 ◽  
Vol 224 ◽  
pp. 04005
Author(s):  
Nikolay Gapon ◽  
Roman Sizyakin ◽  
Marina Zhdanova ◽  
Oksana Balabaeva ◽  
Yigang Cen
Keyword(s):  
Neural Network ◽  
Geometric Model ◽  
Similarity Criterion ◽  
Depth Map ◽  
Experimental Results ◽  
Stereo Pair ◽  
Image Structure ◽  
The Neural Network ◽  
Modern Methods ◽  
Trained Neural Network

This paper proposes a method for reconstructing a depth map obtained using a stereo pair image. The proposed approach is based on a geometric model for the synthesis of patches. The entire image is preliminarily divided into blocks of different size, where large blocks are used to restore homogeneous areas, and small blocks are used to restore details of the image structure. Lost pixels are recovered by copying the pixel values from the source based on the similarity criterion. We used a trained neural network to select the “best like” patch. Experimental results show that the proposed method gives better results than other modern methods, both in subjective and objective measurements for reconstructing a depth map.

Applying RBF Neural Networks and Genetic Algorithms to Nonlinear System Optimization

2012 ◽  
Vol 605-607 ◽  
pp. 2457-2460 ◽  
Author(s):  
Hong Fa Wang ◽  
Xin Ai Xu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Rbf Neural Network ◽  
System Optimization ◽  
Rbf Neural Networks ◽  
Optimal Solutions ◽  
Base Function ◽  
The Neural Network ◽  
Engineering Practices ◽  
Radial Base Function

Nonlinear system optimization is always an issue that needs to be considered in engineering practices and management. In order to obtain optimal solutions without analysis formulas to nonlinear systems, we first construct a radial-base-function (RBF) neural network using the newrb() function in MALTAB 7.0, then train the neural network according to input and output, and finally obtain the solution using a genetic algorithm. Simulated experimental results show that the proposed algorithm is able to achieve optimal solutions with a relatively fast speed of convergence.

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