[go: up one dir, main page]

Ballini et al., 2002 - Google Patents

Learning in recurrent, hybrid neurofuzzy networks

Ballini et al., 2002

Document ID
14692818347749701255
Author
Ballini R
Gomide F
Publication year
Publication venue
2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No. 02CH37291)

External Links

Snippet

A. novel recurrent, hybrid neurofuzzy network is proposed in this paper. This model is composed by two distinct parts: a fuzzy inference system and a neural network. The fuzzy system is constructed from fuzzy set models whose units of the fuzzy system are modeled …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0472Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/10Simulation on general purpose computers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/02Computer systems based on specific mathematical models using fuzzy logic
    • G06N7/023Learning or tuning the parameters of a fuzzy system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Kung et al. A unified systolic architecture for artificial neural networks
US5524176A (en) Fuzzy expert system learning network
US5255348A (en) Neural network for learning, recognition and recall of pattern sequences
Ballini et al. Learning in recurrent, hybrid neurofuzzy networks
Zilouchian Fundamentals of neural networks
Reyneri Weighted radial basis functions for improved pattern recognition and signal processing
CN110956250A (en) Double-memristor Hopfield neural network model with coexisting multiple attractors
Scofield Learning internal representations in the coulomb energy network
Palnitkar et al. A review of adaptive neural networks
Kim et al. On developing an adaptive neural-fuzzy control system
Kim A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation
Ballini et al. A recurrent neuro-fuzzy network structure and learning procedure
Jain et al. Practical applications of computational intelligence techniques
Ballini et al. Heuristic learning in recurrent neural fuzzy networks.
Ballini Equality index and learning in recurrent fuzzy neural networks
Nayak et al. GA based polynomial neural network for data classification
Babri et al. Deep feedforward networks: application to pattern recognition
Papageorgiou et al. Learning algorithms for fuzzy cognitive maps.
Munavalli et al. Pattern recognition for data retrieval using artificial neural network
Prieto et al. Simulation and hardware implementation of competitive learning neural networks
Ballini et al. Recurrent fuzzy neural computation: Modeling, learning and application
Tsang et al. Convergence analysis of a discrete Hopfield neural network with delay and its application to knowledge refinement
Ballini et al. Gradient projection method and equality index in recurrent neural fuzzy network
Karayiannis et al. Neural network architectures and learning schemes
Chakrabartty et al. Neuromorphic Computing with AER using Time-to-Event-Margin Propagation