An autonomous, adaptive optimization control system with cooperative fuzzy reasoning and a neural network for large scale process operations

2002 ◽  
Author(s):  
H. Matsumoto ◽  
Y. Ohsawa
Keyword(s):  
Neural Network ◽  
Control System ◽  
Large Scale ◽  
Fuzzy Reasoning ◽  
Adaptive Optimization ◽  
Optimization Control ◽  
Process Operations

Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system

2019 ◽  
Vol 234 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Chih-Hong Lin ◽  
Kuo-Tsai Chang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Gegenbauer Polynomials ◽  
Swarm Optimization ◽  
Synchronous Reluctance Motor ◽  
Optimization Control ◽  
Gegenbauer Polynomial ◽  
Variable Transmission

To cut down influence of nonlinear time-varying uncertainty action in a synchronous reluctance motor driving continuously variable transmission system, an admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system is posed for improving control performance. The admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system involves an observer control, a recurrent Gegenbauer polynomial neural network control and a remunerated control. The weights of recurrent Gegenbauer polynomials neural network controller are regulated by using the adaptive law and the gradient descent technology. The remunerated control with a reckoned law is derived and computed by means of the Lyapunov stability theorem so as to pledge stability of the control system. Likewise, to speedup convergence of weights in the recurrent Gegenbauer polynomial neural network, the mended particle swarm optimization algorithm is used for regulating two kinds of learning rates. At last, three kinds of experimental results are demonstrated to confirm the usefulness of the put forward control system with comparative studies.

scholarly journals Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Baoping Zou ◽  
Jianxiu Wang ◽  
Zhanyou Luo ◽  
Lisheng Hu
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Control System ◽  
Index System ◽  
Control Model ◽  
Quality Control System ◽  
Control Technology ◽  
Construction Quality ◽  
Optimization Control ◽  
Smooth Blasting

The construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction quality control. This paper presents the construction principle of a tunnel smooth blasting quality control system, introduces a process quality control technology into quality control of tunnel smooth blasting construction, designs a framework for the tunnel smooth blasting quality control system, and collects control index data based on field investigation, expert consultation, and experimental research. By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. The following are the conclusions of this study: (1) This paper presented a method of constructing a tunnel smooth blasting quality control index system and established this system with seven criteria layers, namely, geological conditions, explosive properties, borehole parameters, charge parameters, method of initiation, tunnel parameters, and construction factors, as well as a total of nine indexes. (2) The comprehensive optimization control model of BP-ANN, ANFIS, and ENN for tunnel smooth blasting quality was established. (3) The uniform design method was used to optimize the blasting parameters of the tunnel section, which needs to be controlled, and verify that the construction of these comprehensive optimization control models can change the focus of tunnel smooth blasting quality control from the traditional single index control method into a dynamic, intelligent, pluralistic, and integrated control technology.

GA-Based Fuzzy Neural Network for Adaptive Fuzzy Modeling and Control

1998 ◽  
Author(s):  
Tongjian Chen ◽  
Yonghong Peng ◽  
Weiqiang Xie ◽  
Hongming Deng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Knowledge Base ◽  
Fuzzy Neural Network ◽  
Adaptive Fuzzy Control ◽  
Fuzzy Reasoning ◽  
Reasoning Process ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

Abstract Fuzzy logic theory has provided a model-free tool to develop intelligent control system for complex industrial processes by means of simulating the fuzzy reasoning process of human being. However, the performance of such a control system depends on the knowledge base (control rules and membership functions of fuzzy sets). For the control of complex industrial process in which the dynamic parameters of process is time-varying and non-linear, it is necessary to modify and optimize the knowledge base on-line. Adaptive fuzzy control provides a efficient approach for this objective. In this paper, a new fuzzy neural network (FNN) and an adaptive learning mechanism based on genetic algorithm has been proposed for modeling the fuzzy reasoning process and constructing an efficient adaptive fuzzy control systems. Experiment results show that the FNN is capable of modeling complex functions and simulating fuzzy reasoning process of human being.

Special Issue on Fuzzy Control

1995 ◽  
Vol 7 (1) ◽  
pp. 1-1
Author(s):  
Keigo Watanabe ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Large Scale ◽  
Fuzzy Controller ◽  
Fuzzy Reasoning ◽  
Editorial Staff ◽  
Special Issue ◽  
Fuzzy Neural ◽  
Input Variables ◽  
Neural Network Theory

This special issue is devoted to the study of Fuzzy Control applied to robotics and mechatronics. In particular, it contains a collection of fuzzy-neural network approaches, together with the conventional fuzzy reasoning or new approaches. Since the first pioneering work on fuzzy sets and fuzzy logic reported in 1965 by Zadeh, many control application papers have been published with the fundamental fuzzy controllers based on the so-called Mamdani's min-max centroidal method, the TakagiSugeno's functional reasoning, and the simplified reasoning. However, it is recognized that much trial and error is necessary in the design of the conventional fuzzy controller, because the fuzzy reasoning methods mentioned above are not fundamentally related to any control or system theory. In addition, it should be noted that the total number of control rules grows exponentially as the number of input variables to the conventional fuzzy reasoning increases. Thus, in order to improve the conventional approach and develop the new approach for large-scale systems, most current work on fuzzy control is concerned with an effective design, construction, or analysis of the fuzzy controller by invoking the neural network theory, genetic algorithm, and other control or system theories. Although the literature, both in Japanese and in English, on fuzzy control and applications is now very rich, I believe that this special issue provides an important impact on the advanced fuzzy control. This issue would not have been possible without the enthusiastic support of the contributors. I am indebted to all of them for their up-to-date contributions and to the editorial staff for care throughout the editorial and printing process.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Intelligent Lighting Control System in Large-Scale Sports Competition Venues

2018 ◽  
pp. 172-182 ◽  
Author(s):  
Shengmin CAO
Keyword(s):  
Control System ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Practical Significance ◽  
Lighting Control ◽  
Sports Events ◽  
Planning And Design ◽  
Comprehensive Evaluation Model

This paper mainly studies the application of intelligent lighting control system in different sports events in large sports competition venues. We take the Xiantao Stadium, a large­scale sports competition venue in Zaozhuang City, Shandong Province as an example, to study its intelligent lighting control system. In this paper, the PID (proportion – integral – derivative) incremental control model and the Karatsuba multiplication model are used, and the intelligent lighting control system is designed and implemented by multi­level fuzzy comprehensive evaluation model. Finally, the paper evaluates the actual effect of the intelligent lighting control system. The research shows that the intelligent lighting control system designed in this paper can accurately control the lighting of different sports in large stadiums. The research in this paper has important practical significance for the planning and design of large­scale sports competition venues.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Sign in / Sign up

Export Citation Format

Share Document