artificial neural network Javascript library
npm install oya-annOyaAnn is a Javascript library for building, training and using
artificial neural networks (ANNs) that approximate
and model simple multi-variate relationships.
OyaAnn neural networks can be generated dynamically
from observational data
according to need and serialized for later use.
Consider, for example, temperature compensation for conductivity
measurements (i.e., EC/PPM). EC/PPM probes are temperature sensitive
and readings will change with temperature. For example, the EC
of a reference solution having EC=1413 microsiemens @ 25°C
may vary as follows
(Atlas Scientific EC-EZO):
| °C | EC |
| ---- | ---- |
| 5 | 896 |
| 10 | 1020 |
| 15 | 1147 |
| 20 | 1278 |
| 25 | 1413 |
| 30 | 1548 |
| 35 | 1711 |
| 40 | 1860 |
| 45 | 2009 |
| 50 | 2158 |
To complicate matters further, solutions with different dissolved solids will
each exhibit their own individual temperature compensation curves.
Modeling, calibrating and measuring conductivity for different solutions at
different temperatures is therefore a challenge. What is needed is a simple
way to create calibrated models from observed data. Neural networks
are ideal for this task.
With OyaAnn, we can generate custom temperature compensation ANNs
for new nutrient solutions and train them with locally observed data.
Once trained, these ANNs can be archived and re-used as needed.
``js`
var examples = [
new Example([5],[896]),
new Example([10],[1020]),
new Example([15],[1147]),
new Example([20],[1278]),
new Example([25],[1413]),
new Example([30],[1548]),
new Example([35],[1711]),
new Example([40],[1860]),
new Example([45],[2009]),
new Example([50],[2158]),
];
network.train(examples);
to install oya-ann.npm install oya-ann`* OyaAnn is a divergent fork of Kinann repurposed for OyaMist applications.
* OyaAnn wiki...
* mathjs many thanks to MathJS for expression parsing and derivatives!