Parallel Processing, Neural Networks and Genetic Algorithms for Real-Time Robot Control

2011 ◽  
pp. 355-369
Author(s):  
A. Zalzala
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Parallel Processing ◽  
Real Time ◽  
Robot Control

Parallel Processing, Neural Networks and Genetic Algorithms: Revisited

2009 ◽  
Author(s):  
B.H.V. Topping ◽  
J. Sziveri ◽  
A. Bahreininejad ◽  
J.B.P. Leite ◽  
B. Cheng
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Parallel Processing

scholarly journals NEAT Snake: a both evolutionary and neural network adaptation approach

2020 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Vinicius Almeida dos Santos ◽  
Rodrigo Lyra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Real Time ◽  
Relative Position ◽  
Network Adaptation ◽  
The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

Real-Time Neural Network Based Camera Localization and its Extension to Mobile Robot Control

1997 ◽  
Vol 08 (03) ◽  
pp. 279-293 ◽  
Author(s):  
Doo-Hyun Choi ◽  
Se-Young Oh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Real Time ◽  
Robot Control ◽  
Imaging System ◽  
System Modeling ◽  
Hybrid Approach ◽  
Camera Localization ◽  
Mobile Robot Control

The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.

Neural Networks and 3D Edge Genetic Template Matching for Real-Time Face Detection and Recognition

2011 ◽  
pp. 164-177
Author(s):  
Stephen Karungaru ◽  
Minoru Fukumi ◽  
Norio Akamatsu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Real Time ◽  
Face Detection ◽  
Template Matching ◽  
Neural Network Learning ◽  
Face Detection And Recognition ◽  
The One ◽  
Detection And Recognition

This chapter describes a novel system that can track and recognize faces in real time using neural networks and genetic algorithms. The main feature of this system is a 3D facemask that combined with a neural network based face detector and adaptive template matching using genetic algorithms, is capable of detecting and recognizing faces in real time. Neural network learning and template matching enable size and pose invariant face detection and recognition while the genetic algorithm optimizes the searching algorithms enabling real time usage of the system. It is hoped that this chapter will show how and why neural networks and genetic algorithms are well suited to solve complex pattern recognition problems like the one presented in this chapter.

Simple feedback logic, genetic algorithms and artificial neural networks for real-time control of a collection system

1998 ◽  
Vol 38 (3) ◽  
pp. 187-195
Author(s):  
Pavel Hajda ◽  
Vladimir Novotny ◽  
Xin Feng ◽  
Ruoli Yang
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Real Time Control ◽  
Flow Routing ◽  
Time Control ◽  
On Line ◽  
Artificial Neural ◽  
Simple Feedback

This paper describes a pilot-scale implementation of a simple, real-time control (RTC) algorithm based on feedback and also outlines the development and simulation testing of a new RTC methodology that combines genetic algorithms (GAs) and artificial neural networks (ANNs). Computer simulations indicated that the simple feedback logic could reduce pumping by 50 to 80 percent if used to replace the existing RTC system in the test area. Experience with the algorithm after its implementation has confirmed the potential of the algorithm to reduce pumping. Additional simulations with an emerging approach to control (based on GAs) indicated possibilities of reducing pumping still further. Although relatively simple flow routing was used in the GAs, these algorithms do not restrict flow routing to any particular method. If highly accurate flow routing is incorporated, GAs are likely to be rendered too slow for on-line applications. Nevertheless, GAs can still be used, because they can be combined with fast executing on-line algorithms, such as ANNs. This possibility was demonstrated by training a multi-layer ANN to approximate one of the GAs developed. In verification runs the trained ANN provided virtually the same control decisions as did the GA used as the source of the training data.

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