Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

PI Controller: Design

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Reducing Line Loss

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Voicing quantification is more relevant than period perturbation in substitution voices: an advanced acoustical study.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2012

Same author

Tridimensional assessment of adductor spasmodic dysphonia pre- and post-treatment with Botulinum toxin.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2012

Same author

[Perceptive evaluation of substitution voices: the I(I) NFVo rating scale].

Revue de laryngologie - otologie - rhinologie·2006

Same author

Perceptual evaluation of substitution voices: development and evaluation of the (I)INFVo rating scale.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2005

Same author

Detection of spike and wave discharges in the cortical EEG of genetic absence epilepsy rats from Strasbourg.

Physics in medicine and biology·2003

Same author

On the initialization and optimization of multilayer perceptrons.

IEEE transactions on neural networks·1994

Search research articles

Related Experiment Videos

An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons.

J P Martens¹, N Weymaere

¹Electron. and Inf. Syst., Ghent Univ., Gent.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

A new algorithm called equalized error backpropagation (EEBP) speeds up multilayer perceptron (MLP) training. This method enhances accuracy and robustness for large datasets and networks.

Related Experiment Videos

Area of Science:

Machine Learning
Artificial Intelligence
Computational Neuroscience

Background:

Training multilayer perceptrons (MLPs) with error backpropagation (EBP) can be time-consuming, requiring numerous epochs.
On-line training paradigms reduce EBP time but can remain excessive for large networks and datasets.

Purpose of the Study:

Introduce a novel on-line training algorithm, equalized error backpropagation (EEBP).
Improve the speed, accuracy, and robustness of MLP training.
Address challenges associated with training large neural networks on extensive data.

Main Methods:

Developed the equalized error backpropagation (EEBP) algorithm.
Incorporated weight-specific learning rates.
Derived learning rate magnitudes from computable network and data properties.

Main Results:

EEBP demonstrates improved training accuracy compared to standard EBP.
The algorithm significantly reduces the number of training epochs required.
EEBP exhibits enhanced robustness, particularly with badly scaled input data.

Conclusions:

EEBP offers a more efficient and effective method for training MLPs.
The use of adaptive, weight-specific learning rates is key to EEBP's performance.
EEBP is a promising advancement for large-scale neural network training.