Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new learning architecture is proposed for hardware implementation of neural networks. In this architecture, each synaptic ight is intentionally changed for each trial and then modified in proportion to the trial-and-error correlation beten the changes in the ight and the total output error. If the ight changes are small, this learning is almost as good as the backpropagation (BP) learning, without requiring a complex backward network for error backpropagation. If the changes are large, the ights can move in the ight space without being restricted to a relatively small local-minimum. Computer simulation shows that this learning surpasses BP learning in converging to the global minimum when the trial-and-error correlation is defined so as to emphasize the gain (i.e., the decrease in the total output error) rather than the loss. >
['Osamu Fujita']
I am writing a research paper on a idea Trial-and-error correlation learning
[{"from": "human", "value": "I am writing a research paper on a idea Trial-and-error correlation learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new learning architecture is proposed for hardware implementation of neural networks. In this architecture, each synaptic ight is intentionally changed for each trial and then modified in proportion to the trial-and-error correlation beten the changes in the ight and the total output error. If the ight changes are small, this learning is almost as good as the backpropagation (BP) learning, without requiring a complex backward network for error backpropagation. If the changes are large, the ights can move in the ight space without being restricted to a relatively small local-minimum. Computer simulation shows that this learning surpasses BP learning in converging to the global minimum when the trial-and-error correlation is defined so as to emphasize the gain (i.e., the decrease in the total output error) rather than the loss. >"}]