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RB F Neural Network Class
Info
The RB F Neural Network Class is a Library for MetaTrader 5 that class implements neural network of radial basis functions (radial basis function network - rbfn). Here is represented the classic realization of RBFN consisted of two layers of neurons: hidden layer neurons with radially symmetric activation function and exit layer of linear and sigmoid activation function.
Usage
This tool is typically used for enhancing chart analysis and decision making.
Platform
This Library works exclusively on MetaTrader 5 (both build 600+ and newer versions).
Setup
Place the downloaded file in MQL5/Libraries folder via File ? Open Data Folder in MetaTrader 5.
How to Install and Use RB F Neural Network Class
1. Storage: Place library files in the MQL/Libraries directory to ensure they are accessible to your projects.
2. Implementation: Include the library in your code using the #import directive, ensuring you match the exact function names and parameters.
3. Compilation: Ensure the library is present in the directory before you compile your main EA or script, as the compiler links them during this phase.
4. Management: Keep libraries organized in sub-folders if you manage many custom functions to maintain a clean project structure.
Frequently Asked Questions
Q: What is a library file used for? A: Libraries store reusable code modules, allowing you to centralize common logic used by multiple EAs or indicators.
Q: Is a library executable? A: No, libraries are non-executable files containing functions; they must be imported into an EA, indicator, or script to function.
Q: Can I update a library while the platform is running? A: You should compile your EA or script after updating a library to ensure the latest code changes are integrated.
What this tool does
Class implements neural network of radial basis functions (Radial Basis Function Network - RBFN).
Typical Use Case
This Library excels in automated trading and technical analysis on MetaTrader 5.
Compatible Platform & Setup
This Library works on MetaTrader 5. Place the file in the MQL5/Libraries folder and restart the terminal.
Description & Settings
Related: AS Q Neural Net Pure MQ L5 Neural Network Library - another powerful library for MetaTrader 5 traders.
Class implements neural network of radial basis functions (Radial Basis Function Network - RBFN).Also recommended: ML P Neural Network Class - similar library with strong performance on MetaTrader 5.
Here is represented the classic realization of RBFN consisted of two layers of neurons: hidden layer neurons with radially symmetric activation function and exit layer of linear and sigmoid activation function.Activation function of the output layer is automatically selected for training the network depending on the test output data. For the range of -1 to 1 the hyperbolic tangent applies, to the range 0 .. 1 sigmoid applies. If the test output data is beyond the range -1 .. 1, the activation function is not used.
Creation of the network is declared to be the class parametric constructor.
When creating a network the maximum number of neurons in the hidden layer is set which can be used by the network. The actual number of neurons required is determined by learning network.
Learning network is provided by calling the Learn method (the number of learning patterns, input data array, output data array, the number of learning cycles, the maximum learning error).
Input and output learning data are located in one-dimensional arrays vector by vector. The learning process is limited either by the number of learning epochs or admissible error.
The Learn method returns the following values:
0 - complete network learning and learning result can be checked through the class variables: mse – learning error, epoch – number of accomplished learning cycles and neurons - number of neurons in the hidden layer; -3 - not enough neurons in the hidden layer;
-4 - not enough memory.
The Calculate method (input vector array, network response array) is used for getting the network response.
Save (open file handle with FILE_WRITE and FILE_BIN flags) and Load (open file handle with FILE_READ and FILE_BIN flags) methods are intended for saving the network to a file and loading the network from the file respectively. Network topology, learning errors and array weights are saved to the file. If parameters of the loaded network topology differ from the parameters of the established network topology, the network will not be loaded and the Load method will return false.
Using of the class is shown in the attached specimens:Test_RBFN_XOR - learning network function "excluding OR", Test_RBFN_MUL_ADD - learning network to multiplication and adding integers. It is implied that class and example files are placed in one folder.
You may also like: PN N Neural Network Class - excellent alternative for library users on MetaTrader 5.
⚠ Limitations & Risk Warning
- This tool is provided for educational and testing purposes only.
- Past performance does not guarantee future results.
- Trading involves substantial risk of loss. Use on a demo account first.
- Results may vary depending on market conditions, broker, and settings.
- We recommend thorough backtesting and forward testing before using with real funds.