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

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

Universal Approximation Using Radial-Basis-Function Networks.

Neural computation·2019

Same author

Complete memory structures for approximating nonlinear discrete-time mappings.

IEEE transactions on neural networks·1997

See all related articles

Search research articles

Related Experiment Videos

Comments on "Classification ability of single hidden layer feedforward neural networks".

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

Classification is achievable with a single hidden layer neural network, as demonstrated in recent research. Similar findings in a broader context were previously established in earlier studies.

Area of Science:

Machine Learning
Artificial Intelligence
Computer Science

Background:

A recent study investigated a specific classification problem.
The paper concluded that a single hidden layer neural network is sufficient for classification tasks.

Discussion:

This work builds upon earlier findings in a more general setting.
The conclusions presented align with previous theoretical and empirical results.

Key Insights:

Single hidden layer neural networks possess adequate capacity for certain classification problems.
Prior research has established similar conclusions in a generalized context.

Outlook:

Further exploration into the minimal network architectures for complex classification tasks is warranted.

Related Experiment Videos

Investigating the theoretical underpinnings of these findings can lead to advancements in neural network design.