Automation, Control and Intelligent Systems

Volume 1, Issue 5, October 2013

  • On the Warning System of Obstacle Avoidance of Embedded Electronic Guide Dogs

    YUE Xiangyu

    Issue: Volume 1, Issue 5, October 2013
    Pages: 99-102
    Received: Aug. 04, 2013
    Accepted:
    Published: Sep. 10, 2013
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    Abstract: With the rapid development of China's transportation, the frequency of traffic accidents is also high. This not only restricted the development of China's transportation greatly, but also threatened to people's safety seriously. In particular, the accidents caused by the blind due to there are more frequent reproduction, so their traffic safety has... Show More
  • Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm

    Reza Narimani, Ahmad Narimani

    Issue: Volume 1, Issue 5, October 2013
    Pages: 103-112
    Received: Aug. 18, 2013
    Accepted:
    Published: Sep. 20, 2013
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    Abstract: This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such a... Show More
  • A Comparative Evolutionary Models for Solving Sudoku

    A. A. Ojugo., D. Oyemade., R. E. Yoro., A. O. Eboka., M. O. Yerokun, E. Ugboh

    Issue: Volume 1, Issue 5, October 2013
    Pages: 113-120
    Received: Jul. 08, 2013
    Accepted:
    Published: Nov. 10, 2013
    Downloads:
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    Abstract: Evolutionary algorithms have become robust tool in data processing and modeling of dynamic, complex and non-linear processes due to their flexible mathematical structure to yield optimal results even with imprecise, ambiguity and noise at its input. The study investigates evolutionary algorithms for solving Sudoku task. Various hybrids are presente... Show More