Nothing Special   »   [go: up one dir, main page]

Anderson et al., 1994 - Google Patents

Reinforcement learning with modular neural networks for control

Anderson et al., 1994

View PDF
Document ID
6908971853242711796
Author
Anderson C
Hong Z
Publication year
Publication venue
Proceedings of NNACIP

External Links

Snippet

Reinforcement learning methods can be applied to control problems with the objective of optimizing the value of a function over time. They have been used to train single neural networks that learn solutions to whole tasks. Jacobs and Jordan 5 have shown that a set of …
Continue reading at www.cs.colostate.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/02—Computer systems based on specific mathematical models using fuzzy logic

Similar Documents

Publication Publication Date Title
Lin et al. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems
Espinosa et al. Constructing fuzzy models with linguistic integrity-afreli algorithm
US6456989B1 (en) Neuro-fuzzy-integrated data processing system
Sulzberger et al. FUN: Optimization of fuzzy rule based systems using neural networks
Lin et al. A neural fuzzy system with linguistic teaching signals
Anderson Q-learning with hidden-unit restarting
Anderson et al. Reinforcement learning with modular neural networks for control
Flitman et al. Linking discrete-event simulation models with expert systems
US5710867A (en) Method for parallel processing of fuzzy logic inference rules and corresponding circuit architecture with fuzzy inputs and outputs
Sun et al. A hybrid model for learning sequential navigation
Mabu et al. Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments
Unal et al. A fuzzy finite state machine implementation based on a neural fuzzy system
Ikemoto et al. Continuous deep Q-learning with a simulator for stabilization of uncertain discrete-time systems
Farag et al. Neuro-fuzzy modeling of complex systems using genetic algorithms
D’ALCHÉ-BUC et al. Rule extraction with fuzzy neural network
Maass On the role of time and space in neural computation
JP3262340B2 (en) Information processing device
JP2559879B2 (en) Fuzzy controller
JPH0428681A (en) Group management control for elevator
JP2559877B2 (en) Fuzzy controller
JP2559878B2 (en) Fuzzy controller
Belanche et al. Fuzzy heterogeneous neural networks for signal forecasting
JP2559880B2 (en) Fuzzy controller
JP2559883B2 (en) Fuzzy controller
JP2559881B2 (en) Fuzzy controller