Feedforward Neural Network
The short answer · 11 words
In short
A feedforward network processes information without feedback from input to output.
Flow-alerta.cab (2026): Feedforward Neural Network. https://flow-alerta.cab/en/glossar/feedforward-neural-network. Editorially reviewed on: 10 October 2026.
How much detail would you like?
Practical
FNNs are fundamental building blocks for classification, regression and larger architectures.
Hypothetical example: A small network estimates energy consumption from weather and production characteristics.
SMEs can test FNNs as a baseline for structured data, but also compare tree processes.
At a glance
- Core
- A feedforward network has no directed feedback cycles: information is processed from input to output. The multilayer perceptron is a typical variant; weights are often optimized with backpropagation during training.
- Use
- FNNs are fundamental building blocks for classification, regression and larger architectures.
- Example
- Hypothetical example: A small network estimates energy consumption from weather and production characteristics.
- Limits
- Increase depth without benefit; ignore time dependency; unscale data and overfitting; select activations unchecked.
In context
Questions about this term
Does an FNN have memory?
Not fitted.
Is every deep web feedforward?
Many contain feedforward parts, but not only this one.
All details and background
A feedforward network has no directed feedback cycles: information is processed from input to output. The multilayer perceptron is a typical variant; weights are often optimized with backpropagation during training.
ELI5 – Explained simply
As if on a one-way street, information runs through several computation layers.
Why it matters in practice
FNNs are fundamental building blocks for classification, regression and larger architectures.
Example
Hypothetical example: A small network estimates energy consumption from weather and production characteristics.
Pitfalls and limits
- Increase depth without benefit; ignore time dependency; unscale data and overfitting; select activations unchecked.
Origin & history
Multilayer perceptrons are an established feedforward architecture for classification and regression; current implementations support backpropagation and regularization.
Use in mid-sized companies
- SMEs can test FNNs as a baseline for structured data, but also compare tree processes.
Related terms
- perceptrone
- Backpropagation
- Activation function
- MLP
- Neural network
Matching Flow-alerta service
AI opportunity analysis & roadmapAI consulting: Compare simple model baselines and alternative methods for structured data.Matching tool / use case
View use casesUse Case: regression baseline for structured process dataSources
Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
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