Can an ANN Use a SR-Latch to Store Fuzzy Floating Point Variables?

In summary: I'll try to do that next time.In summary, the ANN can distinguish between notes on a guitar, but it's not clear if it makes money.
  • #1
ADDA
67
2
I'm attempting to use a artificial neural network to store a fuzzy floating point variable. While writing the code, I became somewhat creative. I used the idea of an Set Reset Latch and statistically translated the hardware SR-latch into an artificial neural network. My mathematical goal is to determine if input is consecutively low or high. To achieve this, I've modified the neural network idea slightly. Since I did not have a way to calculate error (also a reason for thinking this up), I pointed the error vector down in terms of the difference of the second derivative and first derivative of the end layer's nodes, so that the network would converge on less and less change of nodes. Also I've modified this activation function:

1.0 / (1.0 + e^(-x))

to allow for three convergence areas: -1, 0, 1, as seen in the following output of kmPlot:

Screenshot_20170927_163157.png
 
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  • #2
Okay. It is a little hard to read. But. What is your question, I do not seem to see one?
Could you please post a graphic image which would help us older citizens read your output?
 
  • #3
I guess, I was wondering if my idea is valid mathematically. Or is it just creative?

I'm unsure how to post media... other than how I have already. Would you like the equation and explanation, jim mcnamara?
 
  • #4
I've made the following video to show output of my ANN:

The dot pairs represent notes on a guitar: open to the left, low E on the bottom. I start with an open D. The colors represent convergence areas: red (-1), green (0), blue (1). The top dot is one SRANN's output, corresponding to the input of a octave overtone; the bottom dot, an octave fifth overtone. There is an input multiplier in the lower right corner. Magnitude is represented by the alpha channel. A convergence of 0 (green) discards a bright note as being an overtone. The goal is to distinguish between, let's say D3 and D4, or E3 and E4, as played halfway through the video.
 
  • #5
ADDA said:
Does this ANN make Benjamins?
That depends on if you are selling a solution to a problem someone is willing to pay for. There’s a rule-of-thumb that says if you aren't profitable after doing something for a year, it’s just a hobby.
 
  • #6
stoomart said:
That depends on if you are selling a solution to a problem someone is willing to pay for. There’s a rule-of-thumb that says if you aren't profitable after doing something for a year, it’s just a hobby.

Literally, I was being cute. My original title was "Does this ANN make sense." Then pun'd on cents to bills to benjamins.
 
  • #7
FWIW - Since I thought you we trying to make a joke I let it slide. Rather than locking the thread that was basically nonsense. When you expect an answer ask a clear question. You will notice your funny question got no useful answers. This thread is going nowhere. Closed. Please consider asking a clear question.
Thanks.
 
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1. What is an ANN?

An ANN, or artificial neural network, is a type of machine learning algorithm inspired by the structure and function of the human brain. It is used for tasks such as classification, regression, and pattern recognition.

2. How does an ANN make money or generate "Benjamins"?

An ANN does not generate money on its own. It is a tool used by businesses and researchers to make predictions or decisions based on data. The success of an ANN in making money depends on the quality of the data it is trained on and the skill of the person using it.

3. Can an ANN be used for financial forecasting or stock market predictions?

Yes, ANNs have been used for financial forecasting and stock market predictions with varying degrees of success. However, the accuracy of these predictions depends on the quality of the data and the complexity of the market being analyzed.

4. Are there any risks or limitations to using ANNs for financial decisions?

Like any predictive tool, ANNs are not infallible and can make mistakes. They are also limited by the data they are trained on and may struggle with unexpected or outlier data. It is important to thoroughly understand the limitations of an ANN before using it for financial decisions.

5. How do I know if an ANN is the right tool for my financial analysis?

The decision to use an ANN for financial analysis should be based on the specific goals and nature of your project. Consider consulting with a data scientist or expert in machine learning to determine if an ANN is the best approach for your particular situation.

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