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Artificial Intelligence of my Ardui
Artificial Intelligence of my Arduino consists so far of a single soft (programmed) neuron, which I can teach and leave my pot flowers' irrigation in the trust of.
In this instructable we are going to implement an artificial neuron into Arduino. I visited a whole class :) of neurology course, so I can explain it's functioning to you in detail.
A neuron is a unit making decision on the basis of synapse signal (input 0-1) and a weight (importance) of this signal. Say we have a neuron whose synapse is coming from a soil moisture sensor. Weight (excitation threshold) is the level of the signal by which a neuron knows that the soil is dry enough and needs watering.
But every flower has its own comfortable moisture level. That's where a backpropagation can be implemented. By manually watering the flower pot we instruct our neuron what should be the weight of the signal or excitation threshold.
Now you know almost everything a certified neurologist knows about your brains.
In this instructable we are going to implement an artificial neuron into Arduino. I visited a whole class :) of neurology course, so I can explain it's functioning to you in detail.
A neuron is a unit making decision on the basis of synapse signal (input 0-1) and a weight (importance) of this signal. Say we have a neuron whose synapse is coming from a soil moisture sensor. Weight (excitation threshold) is the level of the signal by which a neuron knows that the soil is dry enough and needs watering.
But every flower has its own comfortable moisture level. That's where a backpropagation can be implemented. By manually watering the flower pot we instruct our neuron what should be the weight of the signal or excitation threshold.
Now you know almost everything a certified neurologist knows about your brains.
0/5000
Artificial Intelligence of my Arduino consists so far of a single soft (programmed) neuron, which I can teach and leave my pot flowers' irrigation in the trust of.In this instructable we are going to implement an artificial neuron into Arduino. I visited a whole class :) of neurology course, so I can explain it's functioning to you in detail.A neuron is a unit making decision on the basis of synapse signal (input 0-1) and a weight (importance) of this signal. Say we have a neuron whose synapse is coming from a soil moisture sensor. Weight (excitation threshold) is the level of the signal by which a neuron knows that the soil is dry enough and needs watering.But every flower has its own comfortable moisture level. That's where a backpropagation can be implemented. By manually watering the flower pot we instruct our neuron what should be the weight of the signal or excitation threshold.Now you know almost everything a certified neurologist knows about your brains.
Sedang diterjemahkan, harap tunggu..
Artificial Intelligence dari Arduino saya terdiri sejauh dari yang lembut (diprogram) neuron tunggal, yang saya dapat mengajar dan meninggalkan irigasi pot saya bunga 'dalam kepercayaan.
Dalam instructable ini kita akan menerapkan neuron buatan ke Arduino. Saya mengunjungi seluruh kelas :) tentu saja neurologi, sehingga saya dapat menjelaskan itu berfungsi untuk Anda secara rinci.
Sebuah neuron adalah unit pengambilan keputusan atas dasar sinyal sinaps (input 0-1) dan berat (pentingnya) dari sinyal ini . Katakanlah kita memiliki neuron yang sinaps datang dari sensor kelembaban tanah. Berat (threshold eksitasi) adalah tingkat sinyal dimana neuron tahu bahwa tanah cukup kering dan membutuhkan penyiraman.
Tapi setiap bunga memiliki tingkat kelembaban yang nyaman sendiri. Di situlah backpropagation dapat diimplementasikan. Secara manual menyiram pot bunga kita menginstruksikan neuron kita apa yang seharusnya menjadi berat sinyal atau eksitasi ambang batas.
Sekarang Anda tahu hampir semua ahli saraf bersertifikat tahu tentang otak Anda.
Dalam instructable ini kita akan menerapkan neuron buatan ke Arduino. Saya mengunjungi seluruh kelas :) tentu saja neurologi, sehingga saya dapat menjelaskan itu berfungsi untuk Anda secara rinci.
Sebuah neuron adalah unit pengambilan keputusan atas dasar sinyal sinaps (input 0-1) dan berat (pentingnya) dari sinyal ini . Katakanlah kita memiliki neuron yang sinaps datang dari sensor kelembaban tanah. Berat (threshold eksitasi) adalah tingkat sinyal dimana neuron tahu bahwa tanah cukup kering dan membutuhkan penyiraman.
Tapi setiap bunga memiliki tingkat kelembaban yang nyaman sendiri. Di situlah backpropagation dapat diimplementasikan. Secara manual menyiram pot bunga kita menginstruksikan neuron kita apa yang seharusnya menjadi berat sinyal atau eksitasi ambang batas.
Sekarang Anda tahu hampir semua ahli saraf bersertifikat tahu tentang otak Anda.
Sedang diterjemahkan, harap tunggu..
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