- 10月 11, 2018
- 投稿者: 外国為替ウィキチーム
- カテゴリー: 外国為替取引システム
※ Neural Network ─ Hull Transferring Common (HMA) & 偏差スケール転送コモン (DSMA) ※
☛ HMA アルゴリズムを使用しますが、これは低遅延からゼロ遅延までのバリエーションです。
その後... 次のものと融合する:
↓
☛ Jurik フィルター/スムージングおよびカスタマイズされた MA の種類
☛ Mixed with Deviation-Scaled Transferring Common Algorithm
☛ より高い & 最高のメソッド (より高い & APBの計算)
注意してください: Greatest use with Volumes on Primary Chart indicator ─ advisable for indicator set off/replace motion
Transient principle of Neural Networks:
The Neural community is an adjustable mannequin of outputs as capabilities of inputs. It consists of a number of layers:
- enter layer, which consists of enter knowledge
- hidden layer, which consists of processing nodes known as neurons
- output layer, which consists of 1 or a number of neurons, whose outputs are the community outputs.
All nodes of adjoining layers are interconnected. These connections are known as synapses. Each synapse has an assigned scaling coefficient, by which the info propagated by means of the synapse is multiplied. These scaling coefficient are known as weights (w[私][j][k]). で Feed-Ahead Neural Network (FFNN) the info is propagated from inputs to the outputs. Right here is an instance of FFNN with one enter layer, one output layer, and two hidden layers:
The topology of a FFNN is commonly abbreviated as follows: <# of inputs> - <# of neurons within the first hidden layer> - <# of neurons within the second hidden layer> -...- <# of outputs>. The above community might be known as a 4-3-3-1 community.
The info is processed by neurons in two steps, correspondingly proven throughout the circle by a summation signal and a step signal:
- All inputs are multiplied by the related weights and summed
- The ensuing sums are processed by the neuron's activation operate, whose output is the neuron output.
It's the neuron's activation operate that offers non-linearity to the neural community mannequin. それなしで, there isn't a motive to have hidden layers, and the neural community turns into a linear auto-regressive (AR) mannequin.
✜ 偏差スケール転送コモン (DSMA) ✜
新品 DSMA John Ehlers によって作成され、7 月に掲載されました 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, 暗示からの通常の偏差が、この大きさの尺度として選択されます。. 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, 遅延は最小限ですが、かなりのスムージングを提供する準備ができています. それにもかかわらず, ニューラルネットワーク - HMA & DSMA indicator is fused with Jurik filters/smoothing mixed with zero-lag HMA system.
☝ I can't present any kind of assist like coding (供給コードと一緒に) およびトラブルシューティングサービス.
☢ There are not any ensures that this indicator work completely or with out errors. したがって, 個人の危険を承知で使用する; 私はシステムへの損害に対して法的責任を負わないことに同意します, 金銭的損失、さらには生命の喪失さえも.
final replace:
8:00 午前
木曜日, 11 10月 2018
グリニッジ暗示時間 (GMT)
