- अक्टूबर 11, 2018
- के द्वारा प्रकाशित किया गया: विदेशी मुद्रा विकी टीम
- वर्ग: विदेशी मुद्रा व्यापार प्रणाली
※ Neural Network ─ Hull Transferring Common (एचएमए) & विचलन-स्केल स्थानांतरण सामान्य (डीएसएमए) ※
☛ Makes use of HMA algorithm however this one is a variation from low-lag to zero-lag
after which... अगले के साथ जुड़ गया:
↓
☛ ज्यूरिक फिल्टर/स्मूथिंग और अनुकूलित एमए किस्में
☛ Mixed with Deviation-Scaled Transferring Common Algorithm
☛ उच्चतर & सबसे बढ़िया तरीका (उच्च & एपीबी गणना)
आभास होना: 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 (डब्ल्यू[मैं][जे][क]). में एक Feed-Ahead Neural Network (एफएफएनएन) 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. इसके बिना, there isn't a motive to have hidden layers, and the neural community turns into a linear auto-regressive (एआर) mannequin.
✜ विचलन-स्केल स्थानांतरण सामान्य (डीएसएमए) ✜
एकदम नया डीएसएमए 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, इस परिमाण का माप होने के लिए तात्पर्य से सामान्य विचलन को चुना जाता है. 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. बहरहाल, तंत्रिका नेटवर्क - एचएमए & 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. इसलिए, अपने व्यक्तिगत खतरे पर उपयोग करें; मैं सिस्टम क्षति के लिए कोई कानूनी जिम्मेदारी स्वीकार नहीं करता, मौद्रिक हानि और यहाँ तक कि जीवन की कमी भी.
final replace:
8:00 पूर्वाह्न
गुरुवार, 11 अक्टूबर 2018
ग्रीनविच इंप्लाई टाइम (GMT)
