Real-Time Visual Grasp Synthesis Using Genetic Algorithms and Neural Networks

2007 ◽  
pp. 567-578 ◽  
Author(s):  
Antonio Chella ◽  
Haris Dindo ◽  
Francesco Matraxia ◽  
Roberto Pirrone
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Real Time ◽  
Grasp Synthesis

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.

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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