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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 1: March 2012" : 5 Documents clear
Estimating Processed Cheese Shelf Life with Artificial Neural Networks Sumit Goyal; Gyanendra Kumar Goyal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (802.854 KB)

Abstract

Cascade multilayer artificial neural network (ANN) models were developed for estimating the shelf life of processed cheese stored at 7-8oC.Mean square error , root mean square error,coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models.The developed model with a combination of 5à16à16à1 showed excellent agreement between the actual and the predicted data , thus confirming that multilayer cascade models are good in estimating the shelf life of processed cheese.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.336
Prediction of Future Stock Close Price using Proposed Hybrid ANN Model of Functional Link Fuzzy Logic Neural Model Kumaran Kumar. J; Kailas A
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.671 KB)

Abstract

In this paper, the prediction of future stock close price of SENSEX & NSE stock exchange is found using the proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model. The historic raw data’s of SENSEX & NSE stock exchange has been pre-processed to the range of (0 to 1). After pre-processing the inputs and forwarded to functional expansion function to perform neural operation. The activation function of neuron has fuzzy sets in order to show the future close price range of SENSEX & NSE stock exchange. The model is trained with the pre-processed historic data’s of stock exchange and the prediction rate (Performance & Error rate) of the Proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model is calculated at the testing phase using the performance metrics (MAPE & RMSE).DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.362
Design and Implementation of Fuzzy Position Control System for Tracking Applications and Performance Comparison with Conventional PID Nader Jamali Soufi Amlashi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4718.673 KB)

Abstract

This paper was written to demonstrate importance of a fuzzy logic controller in act over conventional methods with the help of an experimental model. Also, an efficient simulation model for fuzzy logic controlled DC motor drives using Matlab/Simulink is presented. The design and real-time implementation on a microcontroller presented. The scope of this paper is to apply direct digital control technique in position control system. Two types of controller namely PID and fuzzy logic controller will be used to control the output response. The performance of the designed fuzzy and classic PID position controllers for DC motor is compared and investigated. Digital signal Microcontroller ATMega16 is also tested to control the position of DC motor. Finally, the result shows that the fuzzy logic approach has minimum overshoot, and minimum transient and steady state parameters, which shows the more effectiveness and efficiency of FLC than conventional PID model to control the position of the motor. Conventional controllers have poorer performances due to the non-linear features of DC motors like saturation and friction.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.409
Dynamic Particle Swarm Optimization for Multimodal Function H. Omranpour; M. Ebadzadeh; S. Shiry; S. Barzegar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1181.47 KB)

Abstract

In this paper, a technical approach to particle swarm optimization method is presented. The main idea of the paper is based on local extremum escape. A new definition has been called the worst position. With this definition, convergence and trapping in extremumlocal be prevented and more space will be searched. In many cases of optimization problems, we do not know the range that answer is that.In the results of examine on the benchmark functions have been observed that when initialization is not in the range of the answer, the other known methods are trapped in local extremum. The method presented is capable of running through it and the results have been achieved with higher accuracy.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.367
Predictive Modelling and Optimization of Power Plant Nitrogen Oxides Emission Ilamathi P; V. Selladurai; K. Balamurugan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.994 KB)

Abstract

A predictive modelling of nitrogen oxides emission from a 210 MW coal fired thermal power plant with combustion parameter optimization is proposed. The oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt were studied. The parametric field experiment data were used to build artificial neural network (ANN). The coal combustion parameters were used as inputs and nitrogen oxides as output of the model. The predicted values of the ANN model for full load condition were verified with the actual values. The optimum level of input operating conditions for low nitrogen oxides emission was determined by simulated annealing (SA) approach. The result indicates that the combined approach could be used for reducing nitrogen oxides emission.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.286

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