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

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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...

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...

Scale-Up Processes

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...

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...

Collisions in Multiple Dimensions: Problem Solving

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...

You might also read

Related Articles

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

Sort by

Same author

Artificial neural networks for solving ordinary and partial differential equations.

IEEE transactions on neural networks·2008

Same author

A novel method for automated EMG decomposition and MUAP classification.

Artificial intelligence in medicine·2005

Same author

Mixture model analysis of DNA microarray images.

IEEE transactions on medical imaging·2005

Same author

Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines.

Artificial intelligence in medicine·2005

Same author

A spatially constrained mixture model for image segmentation.

IEEE transactions on neural networks·2005

Same author

A sequential method for discovering probabilistic motifs in proteins.

Methods of information in medicine·2004

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Group updates and multiscaling: an efficient neural network approach to combinatorial optimization.

A Likas¹, A Stafylopatis

¹Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 1, 1996

Summary

This summary is machine-generated.

A novel multiscale method enhances binary Hopfield-type neural networks for optimization problems. This approach accelerates problem-solving and finds feasible solutions for partitioning and covering challenges.

Related Experiment Videos

Last Updated: Jul 7, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Computational Neuroscience
Artificial Intelligence
Operations Research

Background:

Binary Hopfield-type neural networks are utilized for optimization problems.
Existing methods may face challenges in exploring complex state-spaces efficiently.

Purpose of the Study:

To introduce and evaluate a novel multiscale method for binary Hopfield-type neural networks.
To establish the method's effectiveness in solving various optimization problems.

Main Methods:

Development of a multiscale method tailored for binary Hopfield-type neural networks.
Introduction and analysis of the 'group update' concept in relation to multiscaling properties.
Application of the method to partitioning and covering problems via a novel network mapping.

Main Results:

The multiscale approach demonstrates effectiveness in exploring problem state-spaces.
Feasible solutions of acceptable quality were obtained for tested optimization problems.
Significant acceleration in computational speed was observed.

Conclusions:

The proposed multiscale method is a valuable technique for enhancing binary Hopfield-type neural networks.
The 'group update' mechanism contributes to the method's efficiency.
This approach offers a promising direction for tackling complex optimization tasks.