Bousmaha et al., 2022 - Google Patents
Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based …Bousmaha et al., 2022
- Document ID
- 3365308259392136313
- Author
- Bousmaha R
- Hamou R
- Amine A
- Publication year
- Publication venue
- Evolutionary Intelligence
External Links
Snippet
Within neural networks, it is considered a difficult task to find optimal values for the number of hidden neurons and connection weights simultaneously. This is because altering the hidden neurons significantly affects a neural network's structure and increases the difficulty …
- 238000005457 optimization 0 title abstract description 64
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
- G06N3/06—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
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
-
- 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
- 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
- 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
- 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
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hassib et al. | WOA+ BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network | |
Mafarja et al. | Dragonfly algorithm: theory, literature review, and application in feature selection | |
Luo et al. | An improved grasshopper optimization algorithm with application to financial stress prediction | |
Eshtay et al. | Metaheuristic-based extreme learning machines: a review of design formulations and applications | |
Jain et al. | Usability feature optimization using MWOA | |
Leung et al. | A hybrid particle swarm optimization and its application in neural networks | |
Qaraad et al. | Large scale salp-based grey wolf optimization for feature selection and global optimization | |
Asghari et al. | A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems | |
Bousmaha et al. | Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based on Lévy flight | |
García-Ródenas et al. | Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm | |
Hans et al. | Binary multi-verse optimization (BMVO) approaches for feature selection | |
Altay et al. | A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer | |
Sajedi et al. | DGSA: discrete gravitational search algorithm for solving knapsack problem | |
Oregi et al. | Adversarial sample crafting for time series classification with elastic similarity measures | |
Sah et al. | Evaluating pattern classification techniques of neural network using k-means clustering algorithm | |
Han et al. | An overview of high utility itemsets mining methods based on intelligent optimization algorithms | |
Nguyen et al. | nQSV-Net: a novel queuing search variant for global space search and workload modeling | |
Dagaeva et al. | Fuzzy rules reduction in knowledge bases of decision support systems by objects state evaluation | |
Jena et al. | Hybrid decision tree for machine learning: A big data perspective | |
Osei-Kwakye et al. | A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection | |
Sarangi et al. | Training a feed-forward neural network using artificial bee colony with back-propagation algorithm | |
Nayak et al. | Optimizing a higher order neural network through teaching learning based optimization algorithm | |
Litzinger et al. | Compute-efficient neural network architecture optimization by a genetic algorithm | |
Mallick et al. | Feature selection and classification for microarray data using ACO-FLANN framework | |
Anh et al. | Hybrid genetic-bees algorithm in multi-layer perceptron optimization |