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

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.

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.

Intelligent Hybrid System: A Reliability-Based Failure Management Application

2001 ◽  
Vol 5 (6) ◽  
pp. 307-314 ◽  
Author(s):  
Jose Aguilar ◽  
◽  
Mariela Cerrad ◽  
Katiuska Morillo ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Historical Data ◽  
Intelligent System ◽  
The Other ◽  
Hybrid Architecture ◽  
System A ◽  
Reliability Centered Maintenance ◽  
Management Application

The integration of different intelligent techniques (such as Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms, etc.) into a hybrid architecture allows to overcome their individual limitations. In industrial environments, these intelligent techniques can be combined to reach more effective solutions to complex problems. On the other hand, failure management in processes, equipment or plants, acquires more importance in modern industry every day, in order to minimize unexpected faults and guaranties a greater reliability, safety, disposition and productivity in the industry. In this paper, an intelligent system is designed for failure management based on Reliability Centered Maintenance methodology, Fuzzy Logic and Neural Networks. The system proposes the maintenance tasks according to the historical data of the equipment.

The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics

1999 ◽  
Vol 121 (4) ◽  
pp. 607-612 ◽  
Author(s):  
H. R. DePold ◽  
F. D. Gass
Keyword(s):  
Neural Networks ◽  
Expert Systems ◽  
Condition Monitoring ◽  
Intelligent System ◽  
Diagnostic Tools ◽  
Diagnostic Systems ◽  
Recent Developments ◽  
Gas Generators ◽  
Internet Link ◽  
Airline Fleet

Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: (1) application of statistical analysis and artificial neural network filters to improve data quality, (2) neural networks for trend change detection, and classification to diagnose performance change, and (3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

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