Foundations of neural networks
Foundations of neural networks
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neurocontrol and fuzzy logic: connections and designs
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
Digital neural networks
Neural network and fuzzy logic applications in C/C++
Neural network and fuzzy logic applications in C/C++
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Biologically inspired neural network approaches to real-time collision-free robot motion planning
Biologically inspired robot behavior engineering
Implementation of a neural-based navigation approach on indoor and outdoor mobile robots
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Navigation behaviors based on fuzzy ArtMap neural networks for intelligent autonomous vehicles
Advances in Artificial Neural Systems
Communication constraints multi-agent territory exploration task
Applied Intelligence
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The use of Neural Networks (NN) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV) near the human one in recognition,learning, decision-making, and action. First, current navigation approachesbased on NN are discussed. Indeed, these current approaches remedyinsufficiencies of classical approaches related to real-time,autonomy, and intelligence. Second, a neural navigation approach essentially based on patternclassification to acquire target localization and obstacle avoidancebehaviors is suggested. This approach must provide vehicles with capability,after supervised Gradient Backpropagation learning, torecognize both six (06) target location and thirty (30) obstacle avoidancesituations using NN1 and NN2 Classifiers, respectively.Afterwards, the decision-making and action consist of two associationstages, carried out by reinforcement Trial and Error learning, and their coordination using a NN3. Then, NN3allows to decide among five (05) actions (move towards 30°, move towards60°, move towards 90°, move towards 120°, and move towards150°). Third, simulation results which display the ability of theneural approach to provide IAV with capability tointelligently navigate in partially structured environments are presented.Finally, a discussion dealing with the suggested approach and how itrelates to some other works is given.