- ตุลาคม 11, 2018
- โพสโดย: ทีมงาน Forex Wiki
- หมวดหมู่: ระบบการซื้อขายฟอเร็กซ์
※ Neural Network ─ Hull Transferring Common (HMA) & การโอนย้ายขนาดส่วนเบี่ยงเบน (ดีเอสเอ็มเอ) ※
☛ Makes use of HMA algorithm however this one is a variation from low-lag to zero-lag
after which... หลอมรวมกับสิ่งต่อไป:
↓
☛ Jurik Filters/Smoothing and customized MA varieties
☛ Mixed with Deviation-Scaled Transferring Common Algorithm
☛ Higher & วิธีการที่ยิ่งใหญ่ที่สุด (สูงกว่า & การคำนวณ APB)
ระวัง: 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[ฉัน][เจ][เค]). ใน 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. ปราศจากมัน, there isn't a motive to have hidden layers, and the neural community turns into a linear auto-regressive (AR) mannequin.
✜ การโอนย้ายขนาดส่วนเบี่ยงเบน (ดีเอสเอ็มเอ) ✜
แบรนด์ใหม่ ดีเอสเอ็มเอ 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. อย่างไรก็ตาม, โครงข่ายประสาทเทียม - 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 (together with supply code) and troubleshooting service.
☢ There are not any ensures that this indicator work completely or with out errors. ดังนั้น, use at your personal danger; I settle for no legal responsibility for system harm, monetary losses and even lack of life.
final replace:
8:00 AM
วันพฤหัสบดี, 11 ตุลาคม 2018
Greenwich Imply Time (GMT)
