[go: up one dir, main page]

Wang et al., 2008 - Google Patents

Comments on" The extreme learning machine

Wang et al., 2008

View PDF
Document ID
15680617658779142010
Author
Wang L
Wan C
Publication year
Publication venue
IEEE Transactions on Neural Networks

External Links

Snippet

PubTeX output 2008.07.30:1154 Page 1 1494 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 8, AUGUST 2008 Comments and Replies Comments on “The Extreme Learning Machine” Lipo P. Wang and Chunru R. Wan Abstract—This comment …
Continue reading at personal.ntu.edu.sg (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
    • 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
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • 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
    • 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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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

Similar Documents

Publication Publication Date Title
Wang et al. Comments on" The extreme learning machine
Gao et al. NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches
Xie et al. Time series prediction based on NARX neural networks: An advanced approach
Wang et al. Random Fourier filters under maximum correntropy criterion
Naeini et al. Stock market value prediction using neural networks
Wang et al. Robust extreme learning machine in the presence of outliers by iterative reweighted algorithm
CN105243454A (en) Big data-based electrical load prediction system
Luttmann et al. Comparison of backpropagation and Kalman filter-based training for neural networks
Scardapane et al. Kafnets: kernel-based non-parametric activation functions for neural networks
Huang Reply to “comments on “the extreme learning machine””
Lin et al. Adaptive filter design using recurrent cerebellar model articulation controller
NOSRATIP et al. Application of MLP and RBF Methods in Prediction of Travelling within the city
Ghazali et al. Time series prediction using dynamic ridge polynomial neural networks
Zhang et al. Single neuron PID model reference adaptive control based on RBF neural network
Bisoi et al. Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode algorithm and differential evolution
Jo The effect of virtual term generation on the neural-based approaches to time series prediction
Ghazali et al. Dynamic ridge polynomial neural networks in exchange rates time series forecasting
Wang et al. Comments and replies
Airikka Advanced control methods for industrial process control
Kulkarni et al. Generalized neuron: Feedforward and recurrent architectures
Du et al. Perceptrons
Schaefer et al. The Recurrent Control Neural Network.
Uykan Clustering-based algorithms for single-hidden-layer sigmoid perceptron
Damak et al. Indirect adaptive neural control using a hybrid learning algorithm
Lam et al. Synchronization of chaotic systems using neural-network-based controller