Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces reservoir computing for static pattern recognition. Reservoir computing networks are neural networks with a sparsely connected recurrent hidden layer (or reservoir) of neurons. The ights from the inputs to the reservoir and the reservoir ights are ran- domly selected. The ights of the second layer are determined with a linear partial least squares solver. The outputs of the reservoir layer can be considered to be an unsupervised data transformation. This stage has a brain-like plausibility.  shows that by letting the dynamics of the reservoir evolve to a stable solution, and then applying a sigmoid trans- fer function, reservoir computing can be applied as a robust and highly accurate pattern classier. Reservoir computing is applied to 16 dicult multi-class classication benchmark cases, and compared with the best re- sults of state-of the art neural network classication methods with entropic error criteria.
['Mark J. Embrechts', 'Luís A. Alexandre', 'Jonathan D. Linton']
I am writing a research paper on a idea Reservoir computing for static pattern recognition
[{"from": "human", "value": "I am writing a research paper on a idea Reservoir computing for static pattern recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces reservoir computing for static pattern recognition. Reservoir computing networks are neural networks with a sparsely connected recurrent hidden layer (or reservoir) of neurons. The ights from the inputs to the reservoir and the reservoir ights are ran- domly selected. The ights of the second layer are determined with a linear partial least squares solver. The outputs of the reservoir layer can be considered to be an unsupervised data transformation. This stage has a brain-like plausibility.  shows that by letting the dynamics of the reservoir evolve to a stable solution, and then applying a sigmoid trans- fer function, reservoir computing can be applied as a robust and highly accurate pattern classier. Reservoir computing is applied to 16 dicult multi-class classication benchmark cases, and compared with the best re- sults of state-of the art neural network classication methods with entropic error criteria."}]