The Application of Neural Network in Computer Control

2013 ◽  
Vol 380-384 ◽  
pp. 421-424
Author(s):  
Jing Liu ◽  
Yu Chi Zhao ◽  
Xiao Hua Shi ◽  
Su Juan Liu
Keyword(s):  
Neural Network ◽  
Distributed Storage ◽  
Computer Control ◽  
System Optimization ◽  
Good Choice ◽  
Information Storage ◽  
Knowledge Process ◽  
Control Computer ◽  
Self Learning ◽  
Logic System

In recent years, it is a very active direction of research to use neural network to control computer. Neural network is a burgeoning crossing subject, and the way it processes information is different from the past symbolic logic system, which has some unique properties: such as the distributed storage and parallel processing of information, the unity of the information storage and information processing, and have the ability of self-organizing and self-learning. And it has been applied widespread in pattern recognition, signal processing, knowledge process, expert system, optimization, intelligent control and so on. Using neural network can deal with some problems such as complicated environment information, fuzzy background knowledge and undefined inference rules, and it allows samples to have relatively large defects and distortion, so it is a very good choice to adopt the recognizing method of neural network. This thesis discusses the application of neural network in computer control.

Design of Neural Network Controller for Space Robot with Flexible Manipulators

2013 ◽  
Vol 394 ◽  
pp. 393-397
Author(s):  
Jing Ma ◽  
Wen Hui Zhang ◽  
Zhi Hua Zhu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
System Optimization ◽  
Space Robot ◽  
Learning Ability ◽  
Flexible Manipulators ◽  
Neural Network Controller ◽  
Non Linear System ◽  
Self Learning

Neural network self-learning optimization PID control algorithm is put forward for free-floating space robot with flexible manipulators. Firstly, dynamics model of space flexible robot is established, then, neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. Error function is offered by PID controller. The neural network self-learning PID control method can improve the control precision.

Evaluation of a Neural Network Model of Amnesia in Diffuse Cerebral Atrophy

1993 ◽  
Vol 163 (2) ◽  
pp. 217-222 ◽  
Author(s):  
James R. G. Carrie
Keyword(s):  
Neural Network ◽  
Distributed Storage ◽  
Memory Function ◽  
Cerebral Atrophy ◽  
Information Storage ◽  
Memory Storage ◽  
Distributed Network ◽  
Cerebral Disease ◽  
The Brain

A digital computer program generating a simulated neural network was used to construct a model which can show behaviour resembling human associative memory. The experimental network uses distributed storage, and, in this respect, its functional organisation resembles that suggested by reported observations of neuronal activity in the human temporal lobe during memory storage and recall. Inactivation of increasing numbers of randomly distributed network units simulated advancing cerebral atrophy. This caused progressive impairment of performance, resembling the gradual deterioration of memory function observed in chronic diffuse cerebral degeneration. Unit inactivation had similar effects on recall whether the same units were inactivated before or after learning. This differs from most relevant observations of amnesia resulting from diffuse cerebral disease. While the model may functionally resemble long-term information storage sites in the brain, other cerebral mechanisms participating in learning and remembering are also damaged by diffuse cerebral atrophy.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

2013 ◽  
Vol 694-697 ◽  
pp. 1958-1963 ◽  
Author(s):  
Xian Wei ◽  
Jing Dong Zhang ◽  
Xue Mei Qi
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Complex Shape ◽  
Learning Ability ◽  
Zernike Moment ◽  
Recognition Ability ◽  
Identity Based ◽  
Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

THE CONCEPT OF MANAGING THE DATA NETWORK BASED ON SELF-LEARNING SYSTEMS

2020 ◽  
Author(s):  
С.Р. РОМАНОВ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Transmission ◽  
Network Control ◽  
Learning Systems ◽  
Optimal Routing ◽  
Transmission Network ◽  
Data Traffic ◽  
Data Network ◽  
Self Learning

Рассмотрен принцип управления сетью передачи данных (СПД)с помощью искусственной нейронной сети. Предложена концепция проведения вычислений при решении задачи оптимальной маршрутизации трафика данных. Приведен алгоритм управления сетью СПД на базе нейронной сети Хэмминга. The principle of data transmission network control using an artificial neural network is considered. The concept of carrying out calculations when solving the problem of optimal routing of data traffic is proposed. The algorithm for controlling the data transmission network based on the Hamming neural network is presented.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

scholarly journals Design and implementation of an intelligent vision and sorting system

2009 ◽  
Author(s):  
◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Support Vector Machines ◽  
Robot Control ◽  
Support Vector ◽  
Design And Implementation ◽  
The Neural Network ◽  
Vector Machines ◽  
Control Computer ◽  
Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

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