[go: up one dir, main page]

Ahmad et al., 2010 - Google Patents

Performance comparison of gradient descent and Genetic Algorithm based Artificial Neural Networks training

Ahmad et al., 2010

Document ID
4327786529736032436
Author
Ahmad F
Isa N
Osman M
Hussain Z
Publication year
Publication venue
2010 10th International Conference on Intelligent Systems Design and Applications

External Links

Snippet

One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the …
Continue reading at ieeexplore.ieee.org (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
    • G06N3/12—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
    • 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
    • G06N3/082—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
    • G06N3/08—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
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G06N5/025—Extracting rules from data
    • 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
    • G06N5/04—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
    • G06N7/005—Probabilistic networks
    • 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
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • 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
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • 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

Similar Documents

Publication Publication Date Title
Ahmad et al. A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer
Contaldi et al. Bayesian network hybrid learning using an elite-guided genetic algorithm
Sallans et al. Reinforcement learning with factored states and actions
Neruda et al. Learning methods for radial basis function networks
Juang et al. A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling
Ponnapalli et al. A formal selection and pruning algorithm for feedforward artificial neural network optimization
Kolahdoozi et al. A novel quantum inspired algorithm for sparse fuzzy cognitive maps learning
Ahmad et al. Intelligent breast cancer diagnosis using hybrid GA-ANN
Huang et al. CMA evolution strategy assisted by kriging model and approximate ranking
Ahmad et al. Performance comparison of gradient descent and Genetic Algorithm based Artificial Neural Networks training
Alzaeemi et al. Radial basis function neural network for 2 satisfiability programming
Ileană et al. The optimization of feed forward neural networks structure using genetic algorithms
Guillén et al. Minimising the delta test for variable selection in regression problems
Pei et al. A survey on accelerating evolutionary computation approaches
Balicki An adaptive quantum-based multiobjective evolutionary algorithm for efficient task assignment in distributed systems
Coelho et al. A chaotic inertia weight TLBO applied to troubleshooting optimization problems
Shariatzadeh et al. A survey on multi-objective neural architecture search
Hadavandi et al. Developing an evolutionary neural network model for stock index forecasting
Chen et al. Evolutionary multiobjective ensemble learning based on bayesian feature selection
Dragoni et al. SimBa: A novel similarity-based crossover for neuro-evolution
Chaudhuri et al. Job Scheduling Problem Using Rough Fuzzy Multilayer Perception Neural Networks.
Shafi et al. Software quality prediction techniques: A comparative analysis
Kathirvel et al. Hybrid imperialistic competitive algorithm incorporated with hopfield neural network for robust 3 satisfiability logic programming
Semenkin et al. Integration of intelligent information technologies ensembles with self-configuring genetic programming algorithm
Fu et al. Application of an integrated support vector regression method in prediction of financial returns