Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   provide theoretical foundations for a new neural model for singular value decomposition based on an extension of the Hebbian learning rule called the cross-coupled Hebbian rule. The model is extracting the SVD of the cross-correlation matrix of two stochastic signals and is an extension on previous work on neural-network-related principal component analysis (PCA).  prove the asymptotic convergence of the network to the principal (normalized) singular vectors of the cross-correlation and  provide simulation results which suggest that the convergence is exponential. The new model may have useful applications in the problems of filtering for signal processing and signal detection. >
['K.I. Diamantaras', 'Sun-Yuan Kung']
I am writing a research paper on a idea Cross-correlation neural network models
[{"from": "human", "value": "I am writing a research paper on a idea Cross-correlation neural network models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   provide theoretical foundations for a new neural model for singular value decomposition based on an extension of the Hebbian learning rule called the cross-coupled Hebbian rule. The model is extracting the SVD of the cross-correlation matrix of two stochastic signals and is an extension on previous work on neural-network-related principal component analysis (PCA).  prove the asymptotic convergence of the network to the principal (normalized) singular vectors of the cross-correlation and  provide simulation results which suggest that the convergence is exponential. The new model may have useful applications in the problems of filtering for signal processing and signal detection. >"}]