scholarly journals EMERGENCY CONTROL SYSTEM BASED ON NEURAL NETWORKS AND FUZZY LOGIC

2020 ◽  
Vol 1 (1) ◽  
pp. 45-52
Author(s):  
S.M. Konovalov ◽  
◽  
G.A. Yegoshyna ◽  
S.M. Voronoy ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Expert System ◽  
Knowledge Base ◽  
Performance Indicators ◽  
System Performance ◽  
Operation Mode ◽  
Operational Reliability ◽  
Efficiency Coefficient ◽  
The Neural Network

The presented paper investigates the problem of ensuring the safety of modern vessels, represented as complex organizational and technical systems. This study solves the task of diagnosing and predicting the level of ships’ operational reliability using a hybrid expert system based on a combination of a neural network and fuzzy logic. Trends in modern control systems show that they must be adaptive and intelligent. However, these requirements cannot be met by expert systems based only on fuzzy logic. This work explores the possibility of combining neural network modules with fuzzy logic and considers the features of emergency management stages based on the offered hybrid expert system. The input information arrives in a knowledge base through gauges, where it is structured and distributed in the form of performance indicators. Emergency recommendations for the operator are formed as a result of a combination of performance indicators available in the knowledge base. Modules of the neural network and fuzzy logic form a system for assessing a complex technical system’s health based on calculated estimates of the health of technical nodes. In addition, the authors formed a hierarchy of factors affecting the reliability of the system. While developing the knowledge base, critical values for each variable influencing the system performance are set, and when the values are reached, the operation mode becomes an emergency. The authors chose a multilayer perceptron with a layer of recurrent neurons and inputs as fed factors and criteria for performance; one output displays the value of system performance. Prediction of the technical state of the system is made based on time series analysis. The system with six variables was used as a test set, three of which are non-linguistic (efficiency coefficient, temperature, and pressure). The standard linguistic variable, calculated by the neural network, includes speed, fuel consumption, and wear of the node. The fuzzy logic module was used to form recommendations for the prevention or elimination of an emergency.

GTAW Procedure Expert System Based on Neural Network

2013 ◽  
Vol 455 ◽  
pp. 425-430 ◽  
Author(s):  
Xue Wu Wang ◽  
Shang Yong Yang
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Knowledge ◽  
Welding Process ◽  
Learning Ability ◽  
Welding Parameters ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Function Design ◽  
Welding Procedure

Intelligent procedure expert system was developed to select appropriate GTAW procedure in this paper. First, the function design and implementation methods of the welding procedure expert system were introduced. The expert system can present the welding procedure card, multimedia display of welding process, and output function to makes the data sharing more convenient. Then, the database design of the welding procedure expert system based on C/S mode was presented where the expert knowledge was stored. At last, the neural network model was established to realize procedure selection based on the neural network learning ability and the welding case from the database. With the BPNN model, the welding parameters can be obtained based on the input welding conditions.

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.

scholarly journals Analyses of the method development of decisions in an expert system with the use of information from an artificial neural network

2018 ◽  
Vol 178 ◽  
pp. 07002 ◽  
Author(s):  
Stanisław Duer ◽  
Konrad Zajkowski ◽  
Serghei Scaticailov ◽  
Paweł Wrzesień
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Intelligent Systems ◽  
Method Development ◽  
Diagnostic System ◽  
Intelligence System ◽  
Intelligent Maintenance ◽  
The Neural Network ◽  
Artificial Neural

The present article covers the use of an artificial intelligence system in the organization of the prevention of technical objects. For this purpose, the composition of this system including an intelligent diagnostic system and an intelligent maintenance system was characterized and described. An artificial neural network and an expert system, which work among others on the basis of the information developed by the neural network, perform a special function in these systems. It was mentioned in the article that the mathematical model of the regeneration process of the functional properties (prevention) of an object forms the basis of the organization of the prevention activities of technical devices and objects with the use of intelligent systems. This model demonstrated the possibilities and directions for the use of maintenance intelligent systems.

Construction and Functions of Self-Study System for Power Plant Operation

2000 ◽  
Author(s):  
Shengli Tang ◽  
Zuwei He ◽  
Tao Chang ◽  
Liming Xuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Power Plant ◽  
Knowledge Base ◽  
Practical Experience ◽  
Plant Operation ◽  
Self Study ◽  
Power Plant Operation ◽  
Artificial Neural

Abstract In this paper, the Construction and functions of the self-study system for power plant operation is introduced. As a self-study system, it consists of two parts, a simulator and knowledge base. The knowledge base has been built by the combination of expert system and artificial neural network, which supports the system with practical experience and theoretic knowledge. The trainees’ knowledge can be improved by using the system. The realization of the intelligent training function, applications of expert system and artificial neural network are mainly introduced in this paper.

scholarly journals An On-Chip Learning Method for Neuromorphic Systems Based on Non-Ideal Synapse Devices

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1946
Author(s):  
Jae-Eun Lee ◽  
Chuljun Lee ◽  
Dong-Wook Kim ◽  
Daeseok Lee ◽  
Young-Ho Seo
Keyword(s):  
Neural Network ◽  
System Performance ◽  
Learning Method ◽  
Ideal Case ◽  
The Neural Network ◽  
Neuromorphic Systems ◽  
Conductance Changes ◽  
Neuromorphic System ◽  
On Chip ◽  
Conductance Level

In this paper, we propose an on-chip learning method that can overcome the poor characteristics of pre-developed practical synaptic devices, thereby increasing the accuracy of the neural network based on the neuromorphic system. The fabricated synaptic devices, based on Pr1−xCaxMnO3, LiCoO2, and TiOx, inherently suffer from undesirable characteristics, such as nonlinearity, discontinuities, and asymmetric conductance responses, which degrade the neuromorphic system performance. To address these limitations, we have proposed a conductance-based linear weighted quantization method, which controls conductance changes, and trained a neural network to predict the handwritten digits from the standard database MNIST. Furthermore, we quantitatively considered the non-ideal case, to ensure reliability by limiting the conductance level to that which synaptic devices can practically accept. Based on this proposed learning method, we significantly improved the neuromorphic system, without any hardware modifications to the synaptic devices or neuromorphic systems. Thus, the results emphatically show that, even for devices with poor synaptic characteristics, the neuromorphic system performance can be improved.

Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters

2004 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Network Architecture ◽  
Fuzzy Logic Controller ◽  
Rule Base ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Learning Capabilities ◽  
Optimal Signal

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

scholarly journals White-tailed deer sex identification from faecal DNA and pellet morphometry by neural network and fuzzy logic analyses

2019 ◽  
Vol 29 ◽  
pp. 1-12
Author(s):  
Dania Vega ◽  
Sonia Gallina ◽  
Miguel Correa ◽  
M. Parra ◽  
Isaias Chairez
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Faecal Pellet ◽  
Gene Marker ◽  
Faecal Dna ◽  
Sry Gene ◽  
Faecal Pellets ◽  
Non Invasive ◽  
The Neural Network ◽  
Fuzzy Logic Analysis

Knowing the sex of white-tailed deer (Odocoileus virginianus) individuals can provide information to set harvesting rates and management activities. Therefore, the aim of this study is to identify the sex through classification function by using faecal pellet morphometry. Faeces were collected for 12 months in Durango, Mexico; their morphometric variables were measured, the faecal DNA was extracted, and the SRY gene marker was amplified to identify sex. A neural network and fuzzy logic sex classification functions were obtained. The outputs were validated with the SRY gene results. Data from adults in the winter were used to obtain the classification functions. Classification functions could accurately classify sex in 94.4% with neural networks and 86.9% with fuzzy logic. The neural network classified more accurately the sex of adult white-tailed deer studied in winter with the faecal pellets morphometry than with the fuzzy logic analysis. This technique can be a tool for non-invasive studies and monitoring of populations.

scholarly journals The control to aggregates of pumping stations using a regulator based on a neural network with fuzzy logic

2019 ◽  
Vol 102 ◽  
pp. 03007
Author(s):  
Vladlen Kuznetsov ◽  
Sergey Dyadun ◽  
Valentin Esilevsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Simulation Modeling ◽  
Neural Controller ◽  
The Neural Network ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
Pumping Units ◽  
Theory Of Fuzzy Sets

A pumping station control system is considered using a controller based on a fuzzy logic neural network. The simulation of the classical and fuzzy regulators. The possibility of the implementation of the controller in the form of an adaptive multilayer neural network is shown. The use of the theory of fuzzy sets in combination with the theory of neural networks to create a fuzzy-neural regulator to control pumping units provides a promising approach. Simulation modeling and real operation have shown that fuzzy-logic regulators have a number of advantages over classical regulators, which allow the use of form and limitations. Using the neural network model allows you to add the properties of adaptability and learning. The fuzzy-neural controller for controlling pumping units is promising in terms of efficiency and safety by controlling pumping stations.

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