scholarly journals The Streamers Dynamics Study by an Intelligent System Based on Neural Networks

2021 ◽  
Vol 8 ◽  
pp. 1-5
Author(s):  
Fouad Khodja ◽  
Younes Mimoun ◽  
Riad Lakhdar Kherfane
Keyword(s):  
Neural Networks ◽  
Development Process ◽  
Intelligent System ◽  
Electrical Breakdown ◽  
Polymer Surface ◽  
Neural Model ◽  
Laboratory Model ◽  
Insulating Materials ◽  
Insulation Systems ◽  
Numerical Tool

The formation and propagation of streamers is an important precursor to determine the characteristics of electrical breakdown of many HV electrode configurations. Understanding of the study of the interaction between the polymer surface and the development process of the streamer is of major importance when we want to improve internal and external performance insulation systems. In this context, a numerical tool using neural networks is developed. This model allows evaluating the speed of streamers as a function of the amplitude of voltage initiation and the nature of the insulating materials. For this, a database was created to train the neural model from a laboratory model. This investigation builds a database for predicting the propagation of streamers on the polymers surface by different neuronal methods and this presents an interesting tool for estimating the propagation phenomena in functions of very important parameters

Hybrid Legal Intelligent System Using Fuzzy and Neural Networks

2017 ◽  
Vol 5 (11) ◽  
pp. 222-231
Author(s):  
S. Sridevi ◽  
◽  
◽  
P. Venkata Subba Reddy
Keyword(s):  
Neural Networks ◽  
Intelligent System

The TP Chromatic Aberration Estimation by Using Neural Networks

2011 ◽  
Vol 121-126 ◽  
pp. 4239-4243 ◽  
Author(s):  
Du Jou Huang ◽  
Yu Ju Chen ◽  
Huang Chu Huang ◽  
Yu An Lin ◽  
Rey Chue Hwang
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Physical Property ◽  
Chromatic Aberration ◽  
Neural Model ◽  
Artificial Intelligent ◽  
Error Back Propagation ◽  
The Neural Networks ◽  
Simulation Results

The chromatic aberration estimations of touch panel (TP) film by using neural networks are presented in this paper. The neural networks with error back-propagation (BP) learning algorithm were used to catch the complex relationship between the chromatic aberration, i.e., L.A.B. values, and the relative parameters of TP decoration film. An artificial intelligent (AI) estimator based on neural model for the estimation of physical property of TP film is expected to be developed. From the simulation results shown, the estimations of chromatic aberration of TP film are very accurate. In other words, such an AI estimator is quite promising and potential in commercial using.

scholarly journals Semiotic Aggregation in Deep Learning

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1365
Author(s):  
Bogdan Muşat ◽  
Răzvan Andonie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Decision Model ◽  
Deep Neural Networks ◽  
Neural Model ◽  
Network Layers ◽  
Saliency Maps ◽  
Spatial Entropy ◽  
Insight Into

Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In our experiments, we visualize the superization process and show how the obtained knowledge can be used to explain the neural decision model. In addition, we attempt to optimize the architecture of the neural model employing a semiotic greedy technique. To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.

scholarly journals Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Dual Frequency ◽  
Knowledge Based ◽  
Slot Size ◽  
Good Agreement

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

2016 ◽  
pp. 110-124
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Stock Market ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Market Price ◽  
Hybrid Intelligent Systems ◽  
Out Of Sample ◽  
Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

scholarly journals Sustainable, Renewable and Environmental-Friendly Insulation Systems for High Voltages Applications

Molecules ◽  
2020 ◽  
Vol 25 (17) ◽  
pp. 3901
Author(s):  
Muhammad Rafiq ◽  
Muhammad Shafique ◽  
Anam Azam ◽  
Muhammad Ateeq ◽  
Israr Ahmad Khan ◽  
...  
Keyword(s):  
Environmentally Friendly ◽  
Research Trends ◽  
Substantial Part ◽  
Waste Oil ◽  
Market Place ◽  
Insulating Materials ◽  
Economic Issues ◽  
Environmental Friendly ◽  
Insulation Systems ◽  
Petroleum Oil

With the inception of high voltage (HV), requisites on the insulating permanence of HV equipment is becoming increasingly crucial. Mineral/synthetic oil liquid insulation—together with solid insulation materials (paper, pressboard)—is the fundamental insulation constituent in HV apparatuses; their insulation attributes perform a substantial part in a reliable and steady performance. Meanwhile, implications on the environment, scarcity of petroleum oil supplies and discarding complications with waste oil have stimulated investigators to steer their attention towards sustainable, renewable, biodegradable and environmentally friendly insulating substances. The contemporary insulating constituent’s evolution is driven by numerous dynamics—in particular, environmental obligations and other security and economic issues. Consequently, HV equipment manufacturers must address novel specifications concerning to these new standards. Renewable, sustainable and environmentally friendly insulating materials are continuously substituting conventional insulating items in the market place. These are favorable to traditional insulating materials, due to their superior functionality. The also offer explicit security and eco-friendly advantages. This article discusses cutting-edge technology of environmentally friendly insulating materials, including their fabrication, processing and characterization. The new renewable, insulating systems used in HV equipment are submitted and their fundamental gains stated in comparison with conventional insulating materials. Several experimental efforts carried out in various parts of the world are presented, offering an outline of the existing research conducted on renewable insulating systems. The significance of this article lies in summarizing prior investigations, classifying research essence, inducements and predicting forthcoming research trends. Furthermore, opportunities and constraints being experienced in the field of exploration are evidently reported. Last but not least, imminent research proposals and applications are recommended.

Voltage Instability Detection Using Neural Networks

2011 ◽  
pp. 1596-1602
Author(s):  
Adnan Khashman ◽  
Kadri Buruncuk ◽  
Samir Jabr
Keyword(s):  
Neural Networks ◽  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Detection System ◽  
Nonlinear Problems ◽  
Voltage Instability ◽  
Voltage Output

The explosive growth in decision-support systems over the past 30 years has yielded numerous “intelligent” systems that have often produced less-than-stellar results (Michalewicz Z. et al., 2005). The increasing trend in developing intelligent systems based on neural networks is attributed to their capability of learning nonlinear problems offline with selective training, which can lead to sufficiently accurate online response. Artificial neural networks have been used to solve many problems obtaining outstanding results in various application areas such as power systems. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization, where voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This article presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the simulated PDS.

MREM, Discrete Recurrent Network for Optimization

2011 ◽  
pp. 1112-1120
Author(s):  
Enrique Mérida-Casermeiro ◽  
Domingo López-Rodríguez ◽  
Juan M. Ortiz-de-Lazcano-Lobato
Keyword(s):  
Neural Networks ◽  
Combinatorial Optimization ◽  
Image Recognition ◽  
Single Neuron ◽  
Optimization Problems ◽  
Neural Model ◽  
Recurrent Network ◽  
Neural Models ◽  
Seminal Work ◽  
Combinatorial Optimization Problems

Since McCulloch and Pitts’ seminal work (McCulloch & Pitts, 1943), several models of discrete neural networks have been proposed, many of them presenting the ability of assigning a discrete value (other than unipolar or bipolar) to the output of a single neuron. These models have focused on a wide variety of applications. One of the most important models was developed by J. Hopfield in (Hopfield, 1982), which has been successfully applied in fields such as pattern and image recognition and reconstruction (Sun et al., 1995), design of analogdigital circuits (Tank & Hopfield, 1986), and, above all, in combinatorial optimization (Hopfield & Tank, 1985) (Takefuji, 1992) (Takefuji & Wang, 1996), among others. The purpose of this work is to review some applications of multivalued neural models to combinatorial optimization problems, focusing specifically on the neural model MREM, since it includes many of the multivalued models in the specialized literature.

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