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A genetic-fuzzy approach for mobile robot navigation among moving obstacles
TLDR
In this paper, a genetic-fuzzy approach is developed for solving the motion planning problem of a mobile robot in the presence of moving obstacles. Expand
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  • 8
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A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms
TLDR
Fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Expand
  • 90
  • 4
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Evolution of Fuzzy Controllers and Applications
  • D. K. Pratihar, N. Hui
  • Engineering, Computer Science
  • Advances in Evolutionary Computing for System…
  • 2007
TLDR
The present chapter deals with the issues related to the evolution of optimal fuzzy logic controllers (FLC) by proper tuning of its knowledge base (KB), using different tools, such as least-square techniques, genetic algorithms, backpropagation (steepest descent) algorithm, ant-colony optimization, reinforcement learning, Tabu search, Taguchi method and simulated annealing. Expand
  • 11
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Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches
TLDR
Neuro-fuzzy approaches are developed, in the present work, to determine time-optimal, collision-free path of a car-like mobile robot navigating in a dynamic environment. Expand
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A comparative study on some navigation schemes of a real robot tackling moving obstacles
A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and aExpand
  • 53
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Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot
TLDR
This paper presents an analysis on energy consumption of a six-legged robot during its turning gaits of a robot with four different duty factors. Expand
  • 70
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Soft Computing: Fundamentals and Applications
TLDR
Soft Computing: Fundamentals and Applications starts with an introduction to soft computing, a family consists of many members, namely Genetic Algorithms (GAs), Fuzzy Logic (FL), Neural Networks (NNs), and others. Expand
  • 35
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Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding
TLDR
We have developed, in this paper, a genetic-fuzzy system, in which a genetic algorithm (GA) is used to improve the performance of a fuzzy logic controller (FLC). Expand
  • 45
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Modeling of the MIG welding process using statistical approaches
In this paper, an attempt is made to determine input-output relationships of the MIG welding process by using regression analysis based on the data collected as per full-factorial design ofExpand
  • 49
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Tuning of neural networks using particle swarm optimization to model MIG welding process
TLDR
We use particle swarm optimization to tune multi-layer feed-forward neural network for forward and reverse mappings of metal inert gas (MIG) welding process. Expand
  • 63
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