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

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...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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...

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...

You might also read

Related Articles

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

Sort by

Same author

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

How to Evaluate Signal Quality of ear-ECG?

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Impact of Pulmonary Vein Isolation on Atrial Fibrillation Organisation: Correlation of Intracardiac and Surface Electrocardiogram Measures.

Journal of cardiovascular electrophysiology·2025

Same author

UAdam: Unified Adam-Type Algorithmic Framework for Nonconvex Optimization.

Neural computation·2024

Same author

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

IEEE journal of translational engineering in health and medicine·2024

Same author

Physically Meaningful Surrogate Data for COPD.

IEEE open journal of engineering in medicine and biology·2024

Search research articles

Related Experiment Videos

A homomorphic neural network for modeling and prediction.

Maciej Pedzisz¹, Danilo P Mandic

¹Imperial College London, Department of Electrical and Electronic Engineering, Communications and Signal Processing Group, London SW7 2AZ, U.K. m.pedzisz@imperial.ac.uk

Neural Computation

|December 19, 2007

Summary

A novel homomorphic feedforward network (HFFN) offers a viable alternative for nonlinear adaptive filtering. This new model excels in online learning scenarios with small- to medium-scale datasets, demonstrating competitive performance.

Related Experiment Videos

Area of Science:

Signal Processing
Machine Learning
Artificial Neural Networks

Background:

Nonlinear adaptive filtering is crucial for many signal processing applications.
Existing methods like sigmoidal feedforward networks (FFNs) have limitations in certain learning scenarios.

Purpose of the Study:

Introduce a novel homomorphic feedforward network (HFFN) for nonlinear adaptive filtering.
Evaluate the performance and convergence of the HFFN against traditional FFNs.

Main Methods:

Developed a two-layer feedforward architecture with exponential hidden layer and logarithmic preprocessing.
Implemented gradient-based learning for the HFFN architecture.
Conducted simulations on artificial and real-life data for performance comparison.

Main Results:

The HFFN architecture models generalized Volterra systems and homomorphic filters.
Gradient-based learning was successfully applied, with practical considerations for parameter tuning addressed.
HFFN demonstrated comparable or superior performance to FFNs, particularly in online learning on small/medium datasets.

Conclusions:

The proposed HFFN is an effective alternative for nonlinear adaptive filtering.
HFFN shows particular promise for online learning tasks with limited data.
Further research can explore optimal parameter selection and initialization strategies for HFFN.