Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An effective training algorithm called extreme learning machine (ELM) has recently proposed for single hidden layer feedforward neural networks (SLFNs). It randomly chooses the input ights and hidden layer biases, and analytically determines the output ights by a simple matrix-inversion operation. This algorithm can achieve good performance at extremely high learning speed Hover, it may require a large number of hidden units due to non-optimal input ights and hidden layer biases. ,  propose a new approach, evolutionary least-squares extreme learning machine (ELS-ELM), to determine the input ights and biases of hidden units using the differential evolution algorithm in which the initial generation is generated not by random selection but by a least squares scheme. Experimental results for function approximation show that this approach can obtain good generalization performance with compact networks.
['Hieu Trung Huynh', 'Yonggwan Won']
I am writing a research paper on a idea Evolutionary algorithm for training compact single hidden layer feedforward neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Evolutionary algorithm for training compact single hidden layer feedforward neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An effective training algorithm called extreme learning machine (ELM) has recently proposed for single hidden layer feedforward neural networks (SLFNs). It randomly chooses the input ights and hidden layer biases, and analytically determines the output ights by a simple matrix-inversion operation. This algorithm can achieve good performance at extremely high learning speed Hover, it may require a large number of hidden units due to non-optimal input ights and hidden layer biases. ,  propose a new approach, evolutionary least-squares extreme learning machine (ELS-ELM), to determine the input ights and biases of hidden units using the differential evolution algorithm in which the initial generation is generated not by random selection but by a least squares scheme. Experimental results for function approximation show that this approach can obtain good generalization performance with compact networks."}]