Neurale netwerk – HMA & DSMA

※ Neural Network ─ Hull Transferring Common (HMA) & Deviation-Scaled Transferring Common (DSMA) ※

☛ Makes use of HMA algorithm however this one is a variation from low-lag to zero-lag
after which... fused with the next:
↓
☛ Jurik Filters/Smoothing and customized MA varieties
☛ Mixed with Deviation-Scaled Transferring Common Algorithm
☛ Hoër & Greatest Method (Hoër & APB berekening)

Be aware: Greatest use with Volumes on Primary Chart indicator ─ advisable for indicator set off/replace motion

Verbygaande beginsel van neurale netwerke:

The Neural community is 'n verstelbare mannekyn van uitsette as vermoëns van insette. Dit bestaan ​​uit 'n aantal lae:

1. voer laag in, wat bestaan ​​uit betree kennis
2. versteekte laag, which consists of processing nodes known as neurone
3. uitsetlaag, wat bestaan ​​uit 1 of 'n aantal neurone, wie se uitsette die gemeenskapsuitsette is.

Alle nodusse van aangrensende lae is onderling verbind. These connections are known as sinapse. Elke sinaps het 'n toegekende skaalkoëffisiënt, by which the info propagated by means of the synapse is multiplied. These scaling coefficient are known as weights (w[i][j][k]). In 'n Voed-vooruit neurale netwerk (FFNN) the info is propagated from inputs to the outputs. Hier is 'n voorbeeld van FFNN met een invoerlaag, one output layer, and two hidden layers:

Gekoppelde prentjie

The topology of a FFNN is commonly abbreviated as follows: <# van insette> - <# van neurone binne die eerste versteekte laag> - <# van neurone binne die tweede verborge laag> -...- <# van uitsette>. The above community might be known as a 4-3-3-1 gemeenskap.
The info is processed by neurons in two steps, dienooreenkomstig deur die hele sirkel bewys deur 'n opsommingsein en 'n stapsein:

1. Alle insette word vermenigvuldig met die verwante gewigte en opgetel
2. Die daaropvolgende somme word deur die neurone verwerk activation operate, wie se uitset die neuronuitset is.

It's the neuron's activation operate that offers non-linearity to the neural community mannequin. Daarsonder, there isn't a motive to have hidden layers, en die neurale gemeenskap verander in 'n lineêre outoregressief (AR) mannekyn.

✜ Deviation-Scaled Transferring Common (DSMA) ✜

The brand new DSMA made by John Ehlers and featured within the July 2018 situation of TASC journal.

The DSMA is a knowledge smoothing method that acts as an exponential transferring common with a dynamic smoothing coefficient. The smoothing coefficient is mechanically up to date primarily based on the magnitude of value modifications. Within the Deviation-Scaled Transferring Common, the usual deviation from the imply is chosen to be the measure of this magnitude. The ensuing indicator offers substantial smoothing of the info even when value modifications are small whereas shortly adapting to those modifications.

The writer explains that because of its design, it has minimal lag but is ready to present appreciable smoothing. Nietemin, Neurale netwerk - HMA & DSMA indicator is fused with Jurik filters/smoothing mixed with zero-lag HMA system.

Gekoppelde prentjie (klik op om te vergroot)

☝ I can't present any kind of assist like coding (saam met voorsieningskode) en probleemoplossingsdiens.

☢ There are not any ensures that this indicator work completely or with out errors. Daarom, use at your personal danger; Ek aanvaar geen wetlike verantwoordelikheid vir stelselskade nie, geldelike verliese en selfs gebrek aan lewe.

final replace:
8:00 AM
Thursday, 11 Oktober 2018
Greenwich impliseer tyd (GMT)

Gekoppelde lêer

Neural-Network_HMA-DSMA-Jurik.zip 137 KB | 25 aflaaie