A Hybrid System Composed of Neural Networks and Genetic Algorithms

2012 ◽  
Vol 3 (4) ◽  
pp. 45-50
Author(s):  
Dumitrescu Mihaela
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Hybrid Systems ◽  
Large Population ◽  
Complex Data ◽  
Optimal Decisions ◽  
Business Efficiency ◽  
Overall Performance ◽  
And Performance ◽  
Better Than

Neural networks are known for their ability to recognize patterns of noisy, complex data and to estimate a nonlinear relationship between them. But the design of neural networks is very complex because it works on the principle of “black box.” The application of genetic algorithms in neural networks with hybrid systems can improve a network’s ability to make predictions. Hybrid systems involve the use of combined techniques, issues and different models in order to achieve the overall performance better than those offered by each solution considered separately. Projections made by hybrid systems have smaller errors and constructed systems are able to automatically select the variables necessary to function effectively. This is possible due to the principle of selective biological function of genetic algorithms. They select from a large population of neural networks the best generations, made their exchange of elements and even mutations to get the most advanced networks. For an evolving and performance changes economic environment are necessary tools that can help make faster optimal decisions and increase the business efficiency.

scholarly journals Specialization and Performance: Evidence from NCAA 4 × 400 m Relay Times

Economies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 96
Author(s):  
Candon Johnson ◽  
Robert Schultz ◽  
Joshua C. Hall
Keyword(s):  
Performance Measure ◽  
Division I ◽  
Division Iii ◽  
Overall Performance ◽  
The Difference ◽  
And Performance ◽  
Using Data ◽  
The Impact ◽  
Better Than

This paper investigates the impact of having open 400 meter (400 m) runners on NCAA relay teams. Using data from 2012–2016 containing the top 100 4 × 400 m in each NCAA Division relay times for each year, it is found that more 400 m specialists lead to an increase in the overall performance of the team, measured by a decrease in relay times. The effect is examined across Division I–III NCAA track teams. The results are consistent across each division. We view this as a test of the role of specialization on performance. Using runners who specialize in 400 m races should increase overall team performance as long as specialization does not lead to an inefficient allocation of team human capital. An additional performance measure is used examining the difference between projected and actual relay times. Divisions I and II are found to perform better than projected with an increase in 400 m runners, but there is no effect found in Division III.

scholarly journals Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction

2019 ◽  
Vol 22 ◽  
pp. 15-21 ◽  
Author(s):  
Yunus Emre Midilli ◽  
Sergei Parshutin
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Exchange Rate ◽  
Literature Review ◽  
Stock Market ◽  
Design Of Experiments ◽  
Stock Market Prediction ◽  
Input Layer ◽  
And Performance ◽  
Exchange Rate Prediction

Neural networks are commonly used methods in stock market predictions. From the earlier studies in the literature, the requirement of optimising neural networks has been emphasised to increase the profitability, accuracy and performance of neural networks in exchange rate prediction. The paper proposes a literature review of two techniques to optimise neural networks in stock market predictions: genetic algorithms and design of experiments. These two methods have been discussed in three approaches to optimise the following aspects of neural networks: variables, input layer and hyper-parameters.

Engineering industry controllers using neuroevolution

2005 ◽  
Vol 19 (1) ◽  
pp. 49-57 ◽  
Author(s):  
NABIL M. HEWAHI
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Hybrid Systems ◽  
Nonlinear Behavior ◽  
Engineering Industry ◽  
Traditional Methods ◽  
Systems Research ◽  
Linear Behavior ◽  
Temperature And Humidity ◽  
Evolution Algorithms

Neuroevolution, or evolving neural networks with evolution algorithms such as genetic algorithms, is becoming one of the hottest areas in hybrid systems research. One of the areas that become under research using neuroevolutions is the controllers. In this paper, we shall present two engineering controllers based on neuroevolutions techniques. One of the controllers is used to monitor the temperature and humidity in an industry. This controller is having a linear behavior. The second controller is concerned with scheduling parts in queues in an industry. The scheduling controller is having a nonlinear behavior. The results obtained by the proposed controllers based on neuroevolution are compared with results obtained by traditional methods such as neural networks with backpropagation and ordinary simulation for the controller. The results show that the neuroevolution approaches outperform the results obtained by other methods.

Computational Intelligence as a Tool for Small Modular Reactors

2011 ◽  
Author(s):  
Milos Manic ◽  
Piyush Sabharwall
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Nuclear Engineering ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Small Modular Reactors ◽  
Engineering Problems ◽  
And Performance

Computational intelligence techniques (CITs) traditionally consist of artificial neural networks (ANNs), fuzzy systems and genetic algorithms. This article overviews diverse implementations of ANNs, which are the most prominent in nuclear engineering problems, especially for small modular reactors (SMRs). Advanced computational intelligence-based tools will allow data to be transformation into knowledge, thus improving understanding, predictability (can be seen from the two case studies for thermal-hydraulic prediction), sustainability, and performance of SMRs with real time analysis and monitoring.

scholarly journals An Evolutionary Perspective of the Relationship Between Corporate Strategy and Performance, Through the Use of Artificial Neural Networks and Genetic AlgorithmsHttp://Dx.Doi.Org/10.5585/Riae.V9i3.1689

2011 ◽  
Vol 9 (3) ◽  
pp. 63-87
Author(s):  
Alexandre Teixeira Dias ◽  
Carlos Alberto Gonçalves ◽  
Gustavo Ferreira Mendes de Souza
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Corporate Strategy ◽  
Evolutionary Perspective ◽  
Corporate Strategies ◽  
Artificial Neural ◽  
Improved Performance ◽  
And Performance ◽  
The Relationship

This study aims to contribute to the understanding of the relationship between Corporate Strategy and Performance, from the perspective of the Evolutionary Theory. As methods of data processing, obtained in secondary databases, we used artificial neural networks and genetic algorithms. The results of processing neural networks and genetic algorithms demonstrate the importance of corporate strategies in determining performance. The evolutionary perspective emphasizes the importance of investing in operations as a factor influencing the adequacy of the organization, in order to achieve an improved performance, in addition to establishing relationships with other organizations, through members of the board.

scholarly journals AGCS Technique to Improve the Performance of Neural Networks

2019 ◽  
Vol 29 (1) ◽  
pp. 1235-1245
Author(s):  
Kishor Kumar Katha ◽  
Suresh Pabboju
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Cuckoo Search ◽  
Overall Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
In The Beginning ◽  
Better Than

Abstract In this paper, a fresh method is offered regarding training of particular neural networks. This technique is a combination of the adaptive genetic (AG) and cuckoo search (CS) algorithms, called the AGCS method. The intention of training a particular artificial neural network (ANN) is to obtain the finest weight load. With this protocol, a particular weight is taken into account as feedback, which is optimized by means of the hybrid AGCS protocol. In the beginning, a collection of weights is initialized and the similar miscalculation is discovered. Finally, during training of an ANN, we can easily overcome the training complications involving ANNs and also gain the finest overall performance with training of the ANN. We have implemented the proposed system in MATLAB, and the overall accuracy is about 93%, which is much better than that of the genetic algorithm (86%) and CS (88%) algorithm.

scholarly journals COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION

2020 ◽  
Vol 16 (2) ◽  
pp. 135-140
Author(s):  
Tyas Setiyorini ◽  
Frieyadie Frieyadie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Electricity Consumption ◽  
Nonlinear Problems ◽  
Complex Data ◽  
The Neural Network ◽  
The Neural Networks ◽  
Linear Regressions ◽  
Better Than

Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions.  This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

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