The invention discloses a memristive
neuron neuromorphic dynamics prediction method based on a
hybrid machine learning framework. The memristive
neuron neuromorphic dynamics prediction method is suitable for third-order and above memristive
neuron systems with external stimulation. According to the method, an improved next-generation reserve
pool is integrated to calculate MNGRC and XGBoost regression algorithms, and a dual-path prediction architecture specially designed for partial state
observable scenes is constructed. The architecture can realize high-precision prediction of complex neuromorphic dynamic behaviors of third-order memristive neurons under the condition that only a single
state variable is observed. According to the technical scheme, the double-layer secondary reserve
pool design and the XGBoost state
estimation mechanism work cooperatively, the dependence of a traditional method on complete state information is effectively overcome, and the accuracy and stability of long-term prediction in the chaos marginal area are remarkably improved. The method shows excellent cross-mode generalization ability, and accurate prediction of multiple types of neuromorphic behaviors including resting,
periodic oscillation,
chaotic oscillation and complex burst
modes can be realized by only needing a small amount of training data of several representative behaviors; and a key
technical support is provided for the practical application of the neuromorphic computing
system in resource-constrained environments such as
edge computing and the like.