[go: up one dir, main page]

Barakat et al. - Google Patents

A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge

Barakat et al.

View PDF
Document ID
6140569754201543980
Author
Barakat S
Radwan E
Publication venue
International Journal of Computer Applications

External Links

Snippet

The need to handle uncertainty and vagueness in real world becomes a necessity for developing good and efficient systems. Fuzzy rules and their usage in fuzzy systems help too much in solving these problems away from the complications of probability mathematical …
Continue reading at www.academia.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
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • 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
    • G06N3/12—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
    • 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
    • G06N7/023—Learning or tuning the parameters of a fuzzy system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis

Similar Documents

Publication Publication Date Title
Meuth et al. A proposition on memes and meta-memes in computing for higher-order learning
Duch et al. Optimization and global minimization methods suitable for neural networks
Ma An Efficient Optimization Method for Extreme Learning Machine Using Artificial Bee Colony.
Lin et al. Efficient self-evolving evolutionary learning for neurofuzzy inference systems
Zhao et al. A two-stage differential evolutionary algorithm for deep ensemble model generation
Farooq Genetic algorithm technique in hybrid intelligent systems for pattern recognition
Rabani et al. An optimized firefly algorithm based on cellular learning automata for community detection in social networks
Barakat et al. A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge
Garciarena et al. Redefining neural architecture search of heterogeneous multinetwork models by characterizing variation operators and model components
Pal et al. Pattern recognition: Evolution of methodologies and data mining
Kasabov et al. Rule extraction from linguistic rule networks and from fuzzy neural networks: Propositional versus fuzzy rules
Gamal A fuzzy rough rule based system enhanced by fuzzy cellular automata
Raj Improving Classification Accuracy Using Hybrid of Extreme Learning Machine and Artificial Algae Algorithm with Multi-Light Source.
Santana et al. Bio-inspired meta-heuristic as feature selector in ensemble systems: A comparative analysis
Li et al. Sub-network multi-objective evolutionary algorithm for filter pruning
Yen et al. A genetic based fuzzy-neural networks design for system identification
Valdez et al. Neural network optimization with a hybrid evolutionary method that combines particle swarm and genetic algorithms with fuzzy rules
Meena et al. A Novel Method to Auto Configure Convolution Neural Network Model Using Soft Computing Technique to Recognize Telugu Hand-Written Character for Better Accuracy
Berlanga et al. Learning fuzzy rules using genetic programming: context-free grammar definition for high-dimensionality problems
Chia et al. Neural logic network learning using genetic programming
Xu et al. Design interpretable neural network trees through self-organized learning of features
Abou El-Fetouh et al. A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata
Xu et al. Interpretable neural network tree for continuous-feature data sets
Sztandera Fuzzy neural trees
Qinzhen et al. Design Interpretable Neural Network Trees Through Self-organized Learning of Features