Fuzzy NN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Indian Statistical Institute
- Organisation type
- Academia
- Country
- India
- Published
- 1 September 1992
- Authors
- SK Pal, S Mitra
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1.2K
- Training data
- 436 tokens
Table II: "he neural network has three hidden layers, with m hidden nodes in each layer", m = 20, input dim. = 9, output dim. = 6 9*20+20*20+20*20+6*20+66=1166
"The above-mentioned algorithm was tested on a set of 871 Indian Telugu vowel sounds" and 50% of the dataset was used. 871*0.5 ~= 436
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.4 × 10⁹ FLOP
- How it was established
- Operation counting
1166 params * 2 FLOP/param * (3 for forward + backward pass) * 460 epochs * 436 examples
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 1,223
Sources
Where this record came from and when it was last checked.
- Reference
- Multilayer perceptron, fuzzy sets, and classification
- Last updated
- 28 November 2025
What the numbers mean
Background
Fuzzy NN was published by Indian Statistical Institute, in India, in September 1992. It comes out of academia.
It works in Speech, and is recorded as doing speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training it took roughly 1.4 × 10⁹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 436 tokens.
Answers
Fuzzy NN — common questions
How many parameters does Fuzzy NN have?
Fuzzy NN has 1.2K parameters. Table II: "he neural network has three hidden layers, with m hidden nodes in each layer", m = 20, input dim. = 9, output dim. = 6 9*20+20*20+20*20+6*20+66=1166. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Fuzzy NN?
Fuzzy NN was published by Indian Statistical Institute, based in India, categorised as academia.
When was Fuzzy NN released?
Fuzzy NN was published in September 1992. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Fuzzy NN used for?
Fuzzy NN works in Speech, and is recorded as handling speech recognition (ASR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Fuzzy NN?
Around 1.4 × 10⁹ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Fuzzy NN?
None. Fuzzy NN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Fuzzy NN open source?
The licensing for Fuzzy NN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.