神經網路 – 健康管理協會

※ 神經網路 - Hull Transferring Common ※

☛ Jurik Filters/Smoothing and customized MA sorts by mladen
☛ 更高 & Greatest Formulation (更高 & APB計算)
☛ 利用 Hull MA (通過艾倫·赫爾) however this one is a variation from Low lag to Zero lag
☛ Greatest use with Volumes on Fundamental Chart indicator - beneficial for indicator set off/replace motion

Transient principle of Neural Networks:

神經群落 is an adjustable mannequin of outputs as capabilities of inputs. It consists of a number of layers:

1. enter layer, which consists of enter knowledge
2. hidden layer, which consists of processing nodes referred to as neurons
3. 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 referred to as synapses. Each synapse has an assigned scaling coefficient, by which the information propagated by way of the synapse is multiplied. These scaling coefficient are referred to as weights (w[我][j][k]). 在一個 Feed-Ahead Neural Network (FFNN) the information 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:

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The topology of a FFNN is usually abbreviated as follows: <# of inputs> - <# of neurons within the first hidden layer> - <# of neurons within the second hidden layer> -...- <# of outputs>. The above community may be known as a 4-3-3-1 community.
The information is processed by neurons in two steps, correspondingly proven throughout the circle by a summation signal and a step signal:

1. All inputs are multiplied by the related weights and summed
2. The ensuing sums are processed by the neuron's activation perform, whose output is the neuron output.

It's the neuron's activation perform that provides non-linearity to the neural community mannequin. 沒有它, there is no such thing as a motive to have hidden layers, and the neural community turns into a linear auto-regressive (AR) mannequin.

☝ I can't present any sort of assist like coding (together with supply code) and troubleshooting service. 目前, it's possible you'll use this indicator so long as you're armed with the data/talent of the way to use the standard TDI indicator. You may additionally regulate the parameters/settings based mostly in your choice.

☢ By the way in which, there aren't any ensures that these indicators work completely or with out errors. 所以, use at your individual threat; 我同意不對系統損害承擔任何法律責任, 金錢損失甚至生命垂危.

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Add date: 06:24 PM | 週二, 12 六月 2018 | 格林威治標準時間 (格林威治標準時間)

連接文件

Neural-Network_Hull-MA_Jurik.zip 128 知識庫 | 64 下載