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.

scholarly journals Investigation of a Multitasking System for Automatic Ship Berthing in Marine Practice Based on an Integrated Neural Controller

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1167
Author(s):  
Van Suong Nguyen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coordinate System ◽  
Numerical Simulations ◽  
Neural Controller ◽  
The Neural Network ◽  
Artificial Neural ◽  
Ship Berthing

In this article, a multitasking system is investigated for automatic ship berthing in marine practices, based on artificial neural networks (ANNs). First, a neural network with separate structures in hidden layers is developed, based on a head-up coordinate system. This network is trained once with the berthing data of a ship in an original port to conduct berthing tasks in different ports. Then, on the basis of the developed network, an integrated mechanism including three negative signs is linked to achieve an integrated neural controller. This controller can bring the ship to a berth on each side of the ship in different ports. The whole system has the ability to berth for different tasks without retraining the neural network. Finally, to validate the effectiveness of the proposed system for automatic ship berthing, numerical simulations were performed for berthing tasks, such as different ports, and berthing each side of the ship. The results indicate that the proposed system shows a good performance in automatic ship berthing.

Neural Networks for Content-Based Image Retrieval

2011 ◽  
pp. 252-272 ◽  
Author(s):  
Brijesh Verma ◽  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
The Neural Network ◽  
Benchmark Database ◽  
Overall Performance ◽  
Critical Literature

This chapter introduces neural networks for Content-Based Image Retrieval (CBIR) systems. It presents a critical literature review of both the traditional and neural network based techniques that are used in retrieving the images based on their content. It shows how neural networks and fuzzy logic can be used in interpretation of queries, feature extraction and classification of features by describing a detailed research methodology. It investigates a neural network based technique in conjunction with fuzzy logic to improve the overall performance of the CBIR systems. The results of the investigation on a benchmark database with a comparative analysis are presented in this chapter. The methodologies and results presented in this chapter will allow researchers to improve and compare their methods and it will also allow system developers to understand and implement the neural network and fuzzy logic based techniques for content based image retrieval.

Research on the Automotive Suspension Based on BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 478-481
Author(s):  
Tai Hao Li ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Air Suspension ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Intelligence Theory ◽  
Neural Network Algorithm ◽  
Automotive Suspension

This article uses the application of artificial intelligence theory to research on the air suspension system, constructing the structure of control system, and the study of the neural network algorithm is simulation for its study of results. The fusion of fuzzy logic and neural network consist of the fuzzy neural network, which has the advantages of fuzzy logic and neural network.

scholarly journals Deep neo-fuzzy neural network and its learning

2019 ◽  
Vol 1 (92) ◽  
pp. 3-8
Author(s):  
E.V. Bodyansky ◽  
Т.Е. Antonenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Fuzzy Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Main Component ◽  
Triangular Membership Function

Optimizing the learning speedof deep neural networks is an extremely important issue. Modern approaches focus on the use of neural networksbased on the Rosenblatt perceptron. But the results obtained are not satisfactory for industrial and scientific needs inthe context of the speed of learning neural networks. Also, this approach stumbles upon the problems of a vanishingand exploding gradient. To solve the problem, the paper proposed using a neo-fuzzy neuron, whose properties arebased on the F-transform. The article discusses the use of neo-fuzzy neuron as the main component of the neuralnetwork. The architecture of a deep neo-fuzzy neural network is shown, as well as a backpropagation algorithmfor this architecture with a triangular membership function for neo-fuzzy neuron. The main advantages of usingneo-fuzzy neuron as the main component of the neural network are given. The article describes the properties of aneo-fuzzy neuron that addresses the issues of improving speed and vanishing or exploding gradient. The proposedneo-fuzzy deep neural network architecture is compared with standard deep networks based on the Rosenblattperceptron.

Fuzzy Chaotic Neural Networks

2011 ◽  
pp. 520-555
Author(s):  
Tang Mo ◽  
Wang Kejun ◽  
Zhang Jianmin ◽  
Zheng Liying
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Fuzzy Number ◽  
Fuzzy Neural Networks ◽  
Dynamic Chaos ◽  
Chaotic Neural Network ◽  
Chaotic Neural Networks ◽  
Fuzzy Neural ◽  
Chaotic Neuron

An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is still obscure. Both fuzzy logic and dynamic chaos are internal features of the human brain. Therefore, to fuse artificial neural networks, fuzzy logic and dynamic chaos together to constitute fuzzy chaotic neural networks is a novel method. This chapter is focused on the new ways of fuzzy neural networks construction and its application based on the existing achievement in this field. Four types of fuzzy chaotic neural networks are introduced, namely chaotic recurrent fuzzy neural networks, cooperation fuzzy chaotic neural networks, fuzzy number chaotic neural networks and self-evolution fuzzy chaotic neural networks. Chaotic recurrent fuzzy neural networks model is developed based on existing recurrent fuzzy neural networks through introducing chaos mapping into the membership layer. As it is a dynamic system, the input of neuron not only processes the information of former monument but also contains chaos maps information which is provided by dynamic chaos. Cooperation fuzzy chaotic neural network is proposed on the basis of simplified T-S fuzzy chaotic neural networks and Aihara chaotic neuron. It realizes fuzzy reasoning process by a neural network structure in which the rule inference part is realized by chaotic neural networks. Then enlightened by fuzzy number neural networks we propose a fuzzy number chaotic neuron, which is obtained by blurring the Aihara chaotic neuron. Using these neurons to construct fuzzy number chaotic neural networks, the mathematical model and weight updating rules are also given. At last, a self-evolution fuzzy chaotic neural network is proposed according to the principle of self-evolution network, which unifies the fuzzy Hopfield neural network constitution method.

Fuzzy Neural Network Based Coal-Rock Interface Recognition

2010 ◽  
Vol 44-47 ◽  
pp. 1402-1406
Author(s):  
Jian Jun Shi ◽  
La Wu Zhou ◽  
Ke Wen Kong ◽  
Yi Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Signal Process ◽  
Network Training ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Rock Interface ◽  
Coal Rock ◽  
Hidden Layer

. In the coal-rock interface recognition (CIR) technology, signal process and recognition are the key parts. A method for CIR based on BP neural networks and fuzzy technique was proposed in this paper. By using the trail-and-error, the hidden layer dimension of the network was decided. Also the network training and weight modification were studied. In order to get a higher identification ratio, fuzzy neural networks (FNN) based data fusion was studied. For CIR, the structure and algorithm of FNN were determined. The results indicated that the test data can be used to train and simulate with the neural network and FNN. And the proposed method can be used in CIR with a higher recognition ratio.

scholarly journals Recognition of Traffic Signs Applying Neural Network

2019 ◽  
Vol 11 (1) ◽  
pp. 145-148
Author(s):  
Zsolt Barnabás Neurohr ◽  
Edit Tóthné Laufer
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Calculation Methods ◽  
Traffic Signs ◽  
Artificial Intelligence Research ◽  
The Neural Network

Abstract Artificial intelligence is one of the most dynamically developing areas of science today. Although it is not yet an integral part of our lives to use artificial intelligence solutions, it can be seen in terms of development, that it will become available to everyone in the coming decades, and not be exclusive for the richest. An important part of artificial intelligence research are the so-called soft calculation methods, the most important of which are fuzzy logic, genetic algorithms and neural networks. In this article, the authors present a method of identifying certain traffic signs with the help of the neural network.

scholarly journals Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory

2021 ◽  
pp. 778-799
Author(s):  
RyongSik O ◽  
Jiangwei Chu ◽  
Zhenwei Sun ◽  
Myongchol Ri ◽  
MyongSu Sim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Automatic Transmission ◽  
Evidence Theory ◽  
Test Results ◽  
The Neural Network ◽  
Initial Symptoms ◽  
Fuzzy Neural

At present, the method of identifying the fault symptoms of various machines by combining the neural network and the D-S evidence theory is attracting attention from researchers because the identification time is fast and the diagnosis is accurate. In this paper, it was mentioned a method for identifying the fault symptoms of automatic transmission by combining these two theories. First, it was mentioned a method for identifying fault symptoms of the automatic transmission by combining a fuzzy neural network and an RBF neural network. Next, it was newly described a method to improve the accuracy of fault symptom identification by the D-S evidence theory. In addition, the accuracy of this method was verified by an experimental method. In the experiment Firstly, two sub neural networks are established to recognize the initial symptoms. That is, the first sub-neural network E1 be used as the fuzzy neural network, the second sub-neural network E2 be used as RBF neural network, respectively, for preliminary symptom recognition. And then, these outputs of the two sub neural networks are used as the evidence space of D-S evidence theory, so the global diagnosis is carried out. The results show that the test results are consistent with the actual fault symptoms. The success rate of fault diagnosis up to 96.3%, therefore, on the identification of the automatic transmission fault symptom, effectiveness, and feasibility of the D-S evidence theory based on information fusion is verified.

scholarly journals Analysis of fuzzy logic methods for forecasting customer churn

2021 ◽  
Vol 1 (2(57)) ◽  
pp. 12-14
Author(s):  
Andrii Papa ◽  
Yevhen Shemet ◽  
Andrii Yarovyi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Resource Constraints ◽  
Fuzzy Neural Networks ◽  
Inference System ◽  
Customer Churn ◽  
Combined Use ◽  
Fuzzy Neural ◽  
Telecommunications Companies

The object of research is the process of predicting the churn of customers of telecommunications companies based on fuzzy logic and neural networks. The research carried out is based on the application of an approach that is implemented through the combined use of fuzzy logic and neural networks. The main assumption of the study is the hypothesis that the use of a fuzzy neural network formed on the basis of fuzzy logic algorithms can improve the accuracy of predicting customer churn relative to available solutions. This result can’t be achieved neglecting the existing resource constraints and requirements, which must be determined separately for each case of research. The relevance of the problem of forecasting customer churn for companies with a large number of users is considered. A model for predicting customer churn is proposed based on the combined use of fuzzy logic and neural networks. The main feature of this approach is that a test sample of normalized data is used at the basis of fuzzy neural networks, which are processed to form the parameters of membership functions that correspond to the inference system, that is, conclusions are made on the basis of a fuzzy logic apparatus. Also, to find the parameters of the membership function, neural network algorithms are used. Such systems can use previously known information, learn, gain new knowledge, predict time series, perform image classification, and besides, they are quite visual to the user. The application of methods of fuzzy logic is considered, they make it possible to obtain a result in the form of a fuzzy inference. The expediency of choosing these methods is explained by the fact that they were previously used in fuzzy automatic control systems and showed sufficiently high quality results. The expediency and prospects of using the proposed approach in the problem of predicting the outflow of customers of telecommunications companies are shown, and the results of software implementation are presented.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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