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.

Computational Intelligence

2010 ◽  
pp. 111-136 ◽  
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Computational Intelligence ◽  
Analytical Methods ◽  
Fuzzy Systems ◽  
Academic Community ◽  
Neural Computing ◽  
New Paradigm ◽  
Combined System ◽  
Robust System

In the 1990s, a new paradigm of science characterized by uncertainty, nonlinearity, and irreversibility and tackling complex problems was generally recognized by the academic community. In this new paradigm, traditional analytical methods are ineffectual, and there is recognition of the need to explore new methods to solve the more flexible, more robust system problems. In 1994 the first Computational Intelligence Conference in Orlando, Florida, US, first combined three different areas, smart neural networks, fuzzy systems and genetic algorithms, not only because the three have many similarities, but also because a properly combined system of the three is more effective than a system generated by one single technical field. Various theories and approaches of computational intelligence including neural computing, fuzzy computing and evolutional computing are comprehensively introduced in this chapter.

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.

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.

Adaptive Neuro-Fuzzy Systems

2011 ◽  
pp. 31-36
Author(s):  
Larbi Esmahi ◽  
Kristian Williamson ◽  
Elarbi Badidi
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Fuzzy Systems ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Tools And Techniques ◽  
Traditional Approaches

Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility of a less precise or softer approach (Klir et al., 1995, pp. 212-242). An approach where precision is not paramount is not only closer to the way humans thought, but may be in fact easier to create as well (Jin, 2000). Thus was born the field of soft computing (Zadeh, 1994). Other techniques were added to this field, such as Artificial Neural Networks (ANN), and genetic algorithms, both modeled on biological systems. Soon it was realized that these tools could be combined, and by mixing them together, they could cover their respective weaknesses while at the same time generate something that is greater than its parts, or in short, creating synergy. Adaptive Neuro-fuzzy is perhaps the most prominent of these admixtures of soft computing technologies (Mitra et al., 2000). The technique was first created when artificial neural networks were modified to work with fuzzy logic, hence the Neuro-fuzzy name (Jang et al., 1997, pp. 1-7). This combination provides fuzzy systems with adaptability and the ability to learn. It was later shown that adaptive fuzzy systems could be created with other soft computing techniques, such as genetic algorithms (Yen et al., 1998, pp. 469-490), Rough sets (Pal et al., 2003; Jensen et al., 2004, Ang et al., 2005) and Bayesian networks (Muller et al., 1995), but the Neuro-fuzzy name was widely used, so it stayed. In this chapter we are using the most widely used terminology in the field. Neuro-fuzzy is a blanket description of a wide variety of tools and techniques used to combine any aspect of fuzzy logic with any aspect of artificial neural networks. For the most part, these combinations are just extensions of one technology or the other. For example, neural networks usually take binary inputs, but use weights that vary in value from 0 to 1. Adding fuzzy sets to ANN to convert a range of input values into values that can be used as weights is considered a Neuro-fuzzy solution. This chapter will pay particular interest to the sub-field where the fuzzy logic rules are modified by the adaptive aspect of the system. The next part of this chapter will be organized as follows: in section 1 we examine models and techniques used to combine fuzzy logic and neural networks together to create Neuro-fuzzy systems. Section 2 provides an overview of the main steps involved in the development of adaptive Neuro-fuzzy systems. Section 3 concludes this chapter with some recommendations and future developments.

scholarly journals Approximation of phenol concentration using novel hybrid computational intelligence methods

2014 ◽  
Vol 24 (1) ◽  
pp. 165-181 ◽  
Author(s):  
Pawel Plawiak ◽  
Ryszard Tadeusiewicz
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Computational Intelligence ◽  
Recurrent Neural Networks ◽  
Phenol Concentration ◽  
Neural Systems ◽  
Feed Forward ◽  
Computational Intelligence Methods ◽  
Lm Algorithm ◽  
Selection Of

Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

Real-time analysis of ships in radar images with neural networks

1995 ◽  
Vol 28 (12) ◽  
pp. 1899-1913 ◽  
Author(s):  
Cesare Alippi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Time Analysis ◽  
Radar Images ◽  
Real Time Analysis
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