[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Bouaziz et al., 2014 - Google Patents

PSO-based update memory for Improved Harmony Search algorithm to the evolution of FBBFNT'parameters

Bouaziz et al., 2014

View PDF
Document ID
10918095478317878917
Author
Bouaziz S
Alimi A
Abraham A
Publication year
Publication venue
2014 IEEE Congress on Evolutionary Computation (CEC)

External Links

Snippet

In this paper, a PSO-based update memory for Improved Harmony Search (PSOUM-IHS) algorithm is proposed to learn the parameters of Flexible Beta Basis Function Neural Tree (FBBFNT) model. These parameters are the Beta parameters of each flexible node and the …
Continue reading at emoda05.softcomputing.net (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
    • G06N3/06—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
    • G06N3/082—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
    • G06N3/12—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
    • 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
    • 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
    • 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
    • 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
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • 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
    • 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
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models

Similar Documents

Publication Publication Date Title
Zhan et al. Learning-aided evolution for optimization
Lin et al. A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications
Stepashko Developments and prospects of GMDH-based inductive modeling
Bohnstingl et al. Neuromorphic hardware learns to learn
Bala et al. Applications of metaheuristics in reservoir computing techniques: a review
Bouaziz et al. A hybrid learning algorithm for evolving flexible beta basis function neural tree model
Kassaymeh et al. Salp swarm optimizer for modeling software reliability prediction problems
Bolaji et al. Training of neural network for pattern classification using fireworks algorithm
Dhahri et al. Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network
Ettaouil et al. Architecture optimization model for the multilayer perceptron and clustering.
Behrang et al. Assessment of electricity demand in Iran's industrial sector using different intelligent optimization techniques
CN111415051A (en) Power material demand plan prediction method based on BP neural network
Ochoa et al. Differential evolution using fuzzy logic and a comparative study with other metaheuristics
Mancilla et al. Optimization of fuzzy logic controllers with distributed bio-inspired algorithms
Parouha et al. An innovative hybrid algorithm for bound-unconstrained optimization problems and applications
Na et al. A novel heuristic artificial neural network model for urban computing
Bouaziz et al. PSO-based update memory for Improved Harmony Search algorithm to the evolution of FBBFNT'parameters
Van Kien et al. Cascade training multilayer fuzzy model for nonlinear uncertain system identification optimized by differential evolution algorithm
Bouaziz et al. Evolving flexible beta basis function neural tree for nonlinear systems
Dhahri et al. Hierarchical particle swarm optimization for the design of beta basis function neural network
Bouaziz et al. Extended immune programming and opposite-based PSO for evolving flexible beta basis function neural tree
Wu et al. An algorithm for solving travelling salesman problem based on improved particle swarm optimisation and dynamic step Hopfield network
Fahmy et al. Adaptive functional-based neuro-fuzzy PID incremental controller structure
Wu et al. Multi-objective differential-based brain storm optimization for environmental economic dispatch problem
Bouaziz et al. Universal approximation propriety of flexible beta basis function neural tree