Call for papers special issue ”Hybrid Intelligent Systems using Fuzzy Logic Neural Networks and Genetic Algorithms

2004 ◽  
Vol 167 (1-4) ◽  
pp. I-II
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Special Issue ◽  
Hybrid Intelligent Systems

Advanced Computational Intelligence in Control Theory and Applications

1999 ◽  
Vol 3 (2) ◽  
pp. 67-67
Author(s):  
Kazuo Tanaka ◽  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Control Theory ◽  
Soft Computing ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Special Issue ◽  
Systems And Control ◽  
And Control

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

Special Issue on Papers Selected in ISIS & SCIS 2003

2004 ◽  
Vol 8 (4) ◽  
pp. 349-349
Author(s):  
Yong-Soo Kim ◽  
◽  
Kwee-Bo Sim ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Genetic Algorithms ◽  
Soft Computing ◽  
Intelligent Control ◽  
Intelligent Systems ◽  
Fuzzy Controller ◽  
Intelligent System ◽  
Wide Spectrum ◽  
Special Issue

This special issue of journal covers a broad field ranging from intelligent systems to robotics. These papers were selected among the papers that were presented at the Joint 4th International Symposium on Advanced Intelligent Systems and 2nd International Conference on Soft Computing and Intelligent Systems which was held in Jeju, Korea on September 25-28, 2003. In the above symposium, there was a wide spectrum of intelligent systems and related topics, including sessions: intelligent systems, intelligent control, fuzzy sets, fuzzy systems, neural networks, robotics, genetic algorithms, image processing, soft computing, artificial life, etc. Many interesting results were presented at the symposium. Among these various papers, this special issue offers a selection of sixteen papers that contribute to advances of intelligent systems in various aspects. The topics that the selected papers deal with are fuzzy controller for the mobile robot control, neural networks and their application to image processing, intelligent control for a robot, intelligent system for probe detection, fuzzy image processing, genetic algorithms, fuzzy clustering for incomplete categorical data, predictive fuzzy controller for an electric four-wheeled vehicle. As guest editors of this special issue, we would like to express our thanks to authors for their contribution, the anonymous referees for their review, and Prof. Kaoru Hirota for his giving the opportunity to publish this special issue.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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.

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