Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Complex behavior by means of dynamical systems for an anthropomorphic robot
Neural Networks - Special issue on organisation of computation in brain-like systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analog and Digital Control System Design: Transfer-function, State-space, and Algebraic Methods
Analog and Digital Control System Design: Transfer-function, State-space, and Algebraic Methods
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolving neural networks through augmenting topologies
Evolutionary Computation
Intelligent locomotion control on sloping surfaces
Information Sciences—Informatics and Computer Science: An International Journal
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Waves, bumps, and patterns in neural field theories
Biological Cybernetics
A recurrent neural network for solving Sylvester equation with time-varying coefficients
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper proposes a control architecture based on neural fields for a relatively complex and unstable dynamical system. The neural field model is capable of addressing goal-based planning problems and has properties, like embedding in an Euclidean space and linear stability, that potentially make it well-fitted for dynamic control tasks. The neural field control architecture is tested with the inverted pendulum problem. The cart-and-pole inverted pendulum is used as a simple biped walking model, where the cart models the center of pressure and the pole models the center of mass. The parameterized (i.e, non-evolved) neural field control architecture is compared against an evolved recurrent neural field controller applied to the same control task. The non-evolved neural field controller performs, in the simulation, better than the evolved recurrent neural network controller. Furthermore, the neural field has a spatial representation which allows an easy visualization of its field potentials.