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

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...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Optimization Problems

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

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

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

IEEE transactions on neural networks·2008

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

Search research articles

Related Experiment Videos

On the initialization and optimization of multilayer perceptrons.

N Weymaere¹, J P Martens

¹Dept. of Electron. and Inf. Syst., Gent Univ.

IEEE Transactions on Neural Networks

|January 1, 1994

Summary

This study introduces a novel method for initializing and determining the optimal size and topology of two-layer perceptrons for pattern recognition. This approach reduces training time and avoids local optima compared to traditional error-backpropagation methods.

Related Experiment Videos

Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

Multilayer perceptrons are crucial for pattern recognition but suffer from time-consuming training and local optima.
Determining optimal network size and topology often requires extensive training of multiple networks.

Purpose of the Study:

To present a method for initializing two-layer perceptron parameters and identifying optimal network architecture without extensive error-backpropagation training.
To enable efficient optimization of initialized networks using standard error-backpropagation (EBP).

Main Methods:

Proposes a novel initialization technique for two-layer perceptron weights.
Introduces a method to identify suitable network size and topology prior to full training.
The method accommodates various hidden layer unit types (concentric, squashing) and output units (squashing), including direct input-output connections.

Main Results:

Demonstrates the effectiveness of the proposed method on various classification tasks.
Compares results against traditional error-backpropagation training from random initial states.
Highlights the method's ability to find suitable network configurations more efficiently.

Conclusions:

The proposed method offers a more efficient approach to designing and training two-layer perceptrons for pattern recognition.
It addresses key challenges of long training times and the difficulty of finding optimal network structures.
This technique can improve the performance and reduce the computational cost of neural network applications.