A neural network training method based on training data, includes receiving training data including sequential data, and selecting a reference hidden node from hidden nodes in a neural network. The method further includes training the neural network based on remaining hidden nodes obtained by excluding the reference hidden node from the hidden nodes, and based on the training data, the remaining hidden nodes being connected with hidden nodes in a different time interval, and a connection between the reference hidden node and the hidden nodes in the different time interval being ignored.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural network training method based on training data, the method comprising: receiving training data comprising sequential data; selecting a reference hidden node from hidden nodes in a neural network; and training the neural network based on remaining hidden nodes obtained by excluding the reference hidden node from the hidden nodes, and based on the training data, the remaining hidden nodes being connected with hidden nodes in a different time interval, and a connection between the reference hidden node and the hidden nodes in the different time interval being ignored. 2. The method of claim 1, wherein the reference hidden node is excluded during the training of the neural network based on an item of the sequential data. 3. The method of claim 1, wherein the selecting comprises: randomly selecting the reference hidden node from the hidden nodes. 4. The method of claim 1, wherein: the selecting comprises randomly selecting another reference hidden node from the hidden nodes in response the training of the neural network based on an item of the sequential data being terminated; and the training comprises training the neural network based on another item of the sequential data, and based on remaining hidden nodes obtained by excluding the other reference hidden node from the hidden nodes. 5. The method of claim 1, wherein the sequential data comprises any one or any combination of voice data, image data, biometric data, and handwriting data. 6. The method of claim 1, further comprising: updating a connection weight applied to the neural network based on a result of the training. 7. The method of claim 1, wherein the training comprises: controlling the neural network to repetitively learn the training data based on a predetermined number of repetitions. 8. The method of claim 1, wherein the neural network is a recurrent neural network, and comprises hidden layers. 9. A non-transitory computer-readable storage medium comprising a program comprising instructions to cause a computer to perform the method of claim 1. 10. A neural network training method based on training data, the method comprising: receiving training data comprising sequential data; selecting a reference hidden node from hidden nodes in a neural network; and training the neural network by excluding the reference hidden node from the hidden nodes during the training of the neural network based on an item of the sequential data. 11. The method of claim 10, wherein: remaining hidden nodes obtained by excluding the reference hidden node from the hidden nodes are connected with hidden nodes in a different time interval; and a connection between the reference hidden node and the hidden nodes in the different time interval is ignored. 12. The method of claim 10, wherein: the selecting comprises randomly selecting another reference hidden node from the hidden nodes in response the training of the neural network based on the item of the sequential data being terminated; and the training comprises training the neural network based on another item of the sequential data, and based on remaining hidden nodes obtained by excluding the other reference hidden node from the hidden nodes. 13. A non-transitory computer-readable storage medium comprising a program comprising instructions to cause a computer to perform the method of claim 10. 14. A neural network training apparatus based on training data, the apparatus comprising: a training data storage configured to store training data comprising sequential data; and a neural network trainer configured to train a neural network by excluding a reference hidden node from hidden nodes in the neural network during the training of the neural network based on an item of the sequential data. 15. The apparatus of claim 14, wherein: remaining hidden nodes obtained by excluding the reference hidden node from the hidden nodes are connected with hidden nodes in a different time interval; and a connection between the reference hidden node and the hidden nodes in the different time interval is ignored. 16. The apparatus of claim 14, wherein the neural network training unit is configured to: train the neural network by excluding another reference hidden node from the hidden nodes during the training of the neural network based on another item of the sequential data, in response to the training of the neural network based on the item of the sequential data being terminated. 17. A data processing apparatus comprising: a trainer configured to train a neural network by excluding a reference hidden node from hidden nodes in the neural network during the training of the neural network based on an item of sequential data. 18. The data processing apparatus of claim 17, wherein connections between the reference hidden node and nodes in an upper layer are ignored. 19. The data processing apparatus of claim 17, wherein connections between the reference hidden node and nodes in a subsequent time interval are ignored. 20. The data processing apparatus of claim 17, the trainer is configured to: train the neural network by excluding an identical reference hidden node from hidden nodes in each time interval during the training of the neural network based on the item of the sequential data.