NeuroBaseLib

The purpose of the NeuroBase project is finding a way using the neural connectionist approach for handling symbolic paradigm.
NeuroBaseLibe is the core library for doing that, it provides the following:-

1. Long Term Memory abstraction
2. Suitable taxonomy for modeling perceptrones
3. Neural symbolization algorithm

For more information, read below resources:-

https://www.researchgate.net/publication/305741693_Multi-objective_symbolic_regression_using_long-term_artificial_neural_network_memory_LTANN-MEM_and_neural_symbolization_algorithm_NSA?utm_source=twitter&rgutm_meta1=eHNsLTdySDdxR3F1UnJCRFJPZENFQ2FqSERaZis3cXU0OGxuMWxPeVZ4c0Q2WDg0R3h2Q3haeDZqVDJXZElWYWxlc21sZlVsaWdqTld6SVFCcVVma014N2JnPT0%3D

https://www.researchgate.net/publication/315868376_Transfer_learning_with_long_term_artificial_neural_network_memory_LTANN-MEM_and_neural_symbolization_algorithm_NSA_for_solving_high_dimensional_multi-objective_symbolic_regression_problems?utm_source=twitter&rgutm_meta1=eHNsLVZCSDB5a0N5RHhaN3ZtSXI2eHVYclF6WHV0Z255STNZZnM3bndUYnArYkNaNk85WWFDTzNERkhpQ3F1WDFBb1dSQzNQV3BGZEVlREdQNEhvWXFKRWZnPT0%3D

https://www.researchgate.net/publication/315335695_Transfer_learning_with_LTANN-MEM_NSA_for_solving_high_dimensional_multi-objective_symbolic_regression_problems?utm_source=twitter&rgutm_meta1=eHNsLUh2cGxxYzBaMGtiSUxtOFg5Q2lycDR3U1o1aDFBVlpPeURDREs5cGJtWFpHejBFTURVaEVnbHEwV0NJVjNDa1VmWG04bHVyNzFJME9PSEFJcHRuVGpnPT0%3D