EVOLUTIONARY METHOD FOR SYNTHESIS SPIKING NEURAL NETWORKS USING THE NEUROPATTHERN MECHANISM
DOI:
https://doi.org/10.15588/1607-3274-2022-3-8Keywords:
spiking neural network, topology, pattern, evolution, synthesis, artificial neural networks, diagnostics.Abstract
Context. The problem of synthesizing pulsed neural networks based on an evolutionary approach to the synthesis of artificial neural networks using a neuropathic mechanism for constructing diagnostic models with a high level of accuracy is considered. The object of research is the process of synthesis of pulsed neural networks using an evolutionary approach and a neuropathic mechanism.
Objective of the work is to develop a method for synthesizing pulsed neural networks based on an evolutionary approach using a neuropathic mechanism to build diagnostic models with a high level of accuracy of work.
Method. A method for synthesizing pulsed neural networks based on an evolutionary approach is proposed. At the beginning, a population of pulsed neural networks is generated, and a neuropathic mechanism is used for their encoding and further development, which consists in separate encoding of neurons with different activation functions that are determined beforehand. So each pattern with multiple entry points can define the relationship between a pair of points. In the future, this simplifies the evolutionary development of networks. To decipher a pulsed neural network from a pattern, the coordinates for a pair of neurons are passed to the network that creates the pattern. The network output determines the weight and delay of the connection between two neurons in a pulsed neural network. After that, you can evaluate each neuromodel after evolutionary changes and check the criteria for stopping synthesis. This method allows you to reduce the resource intensity during network synthesis by abstracting the evolutionary changes of the network pattern from itself.
Results. The developed method is implemented and investigated on the example of the synthesis of a pulsed neural network for use as a model for technical diagnostics. Using the developed method to increase the accuracy of the neuromodel with a test sample by 20%, depending on the computing resources used.
Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow us to recommend it for use in practice in the synthesis of pulsed neural networks as the basis of diagnostic models for further automation of tasks of diagnostics, forecasting, evaluation and pattern recognition using big data. Prospects for further research may lie in the use of a neuropathic mechanism for indirect encoding of pulsed neural networks, which will provide even more compact data storage and speed up the synthesis process.
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