scholarly journals Prediction of Surgery Control Parameters in Cardiology to Optimize the Emission Fraction Values with the Help of Neural Networks

2021 ◽  
Vol 2021 (4(206)) ◽  
pp. 54-72
Author(s):  
O. Kryvova ◽  
L. Kozak ◽  
O. Kovalenko ◽  
L. Nenasheva
Keyword(s):  
Neural Networks ◽  
Control Parameters ◽  
Emission Fraction

The model of compression ignition engine with artificial neural networks

2008 ◽  
Vol 132 (1) ◽  
pp. 44-49
Author(s):  
Krzysztof BRZOZOWSKI ◽  
Jacek NOWAKOWSKI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Compression Ignition ◽  
Control Parameters ◽  
Working Process ◽  
Compression Ignition Engine ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Model Equations

The paper presents an application of artificial neural network in modelling the working process in compression ignition engine. In order to determine the usefulness of proposed method the optimisation task has been formulated. The aim of optimisation process was to find the engine control parameters which enable reduction of the NOx emission. In order to solve the problem, the model equations has to be integrated for values of control parameters whose are given as output from the neural networks implemented.

scholarly journals OPTIMIZATION OF MANAGEMENT OF URBAN LIGHTS WITH THE USE OF NEURAL NETWORKS

THE BULLETIN ◽  
2021 ◽  
Vol 389 (1) ◽  
pp. 14-17
Author(s):  
A.А. Suleimen ◽  
G.B. Kashaganova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
M.C. Ibraev
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Traffic Management ◽  
Urban Traffic ◽  
Control Parameters ◽  
Traffic Flows ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
On The Road

One of the most pressing problems of large cities is the problem of traffic management of vehicles. The reason for this problem is an imperfect way to manage traffic flows. Traffic light regulation is of particular importance in traffic management. Most modern traffic light control systems operate at set time intervals and are not able to cope with the constantly changing situation on the road. A promising direction for solving this problem is to optimize the system using artificial neural networks. The advantage of neural networks is self-learning, which allows the system to adapt to the changing situation on the road. Despite numerous attempts, it has not yet been possible to obtain a high-quality mathematical model of urban traffic management. This model should determine the functional dependence of transport flow parameters on control parameters. Nowadays, traffic flows are regulated everywhere by means of traffic lights. If we can get a fairly accurate mathematical model of traffic flows, we can determine the optimal duration of the traffic signal phases to achieve the maximum capacity of the road network node. A fairly accurate mathematical model of traffic management that works in predictive mode will display an estimate of the optimal control parameters, as well as make correct decisions in emergency situations. Well-known mathematical models of road traffic take into account only the average values of traffic flows, and not the exact number of cars on each road section at a particular time.

scholarly journals Optimizing connection weights in neural networks using hybrid metaheuristics algorithm

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Solution Space ◽  
Feedforward Neural Networks ◽  
Fine Tuning ◽  
Training Methods ◽  
Control Parameters ◽  
Hybrid Metaheuristics ◽  
Hybrid Particle Swarm Optimization ◽  
The Comparative Study

The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.

scholarly journals Quasi-Matrix and Quasi-Inverse-Matrix Projective Synchronization for Delayed and Disturbed Fractional Order Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jinman He ◽  
Fangqi Chen ◽  
Qinsheng Bi
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Sufficient Conditions ◽  
Inverse Matrix ◽  
Projective Synchronization ◽  
Control Parameters ◽  
Complete Synchronization ◽  
External Disturbances ◽  
Modified Projective Synchronization ◽  
Projective Matrix

This paper is concerned with the quasi-matrix and quasi-inverse-matrix projective synchronization between two nonidentical delayed fractional order neural networks subjected to external disturbances. First, the definitions of quasi-matrix and quasi-inverse-matrix projective synchronization are given, respectively. Then, in order to realize two types of synchronization for delayed and disturbed fractional order neural networks, two sufficient conditions are established and proved by constructing appropriate Lyapunov function in combination with some fractional order differential inequalities. And their estimated synchronization error bound is obtained, which can be reduced to the required standard as small as what we need by selecting appropriate control parameters. Because of the generality of the proposed synchronization, choosing different projective matrix and controllers, the two synchronization types can be reduced to some common synchronization types for delayed fractional order neural networks, like quasi-complete synchronization, quasi-antisynchronization, quasi-projective synchronization, quasi-inverse projective synchronization, quasi-modified projective synchronization, quasi-inverse-modified projective synchronization, and so on. Finally, as applications, two numerical examples with simulations are employed to illustrate the efficiency and feasibility of the new synchronization analysis.

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