Fast Learning Neural Network Using Modified Corners Algorithm

In the past we have seen various developments in the philosophy and application of neural networks. We today have backpropagation algorithm, Hopfield networks, perceptrons, etc. All these are very precise tools which model the data very well. But unfortunately, the problem being faced these days is of training the neural network in short span of time, over the test data. All the above mentioned tools may not be useful in various situations where the neural network needs to be trained rapidly. Hence the solutions offered to the same were the corners rule and the associated CC1 to CC4 algorithms. All these had various pros and cons. This paper uses a different type of modeling to represent data and hence solve the problem of fast learning. Here we have taken the help of distance separation of training data and an unknown input to calculate the most probable output in the neural network. This algorithm is better than the others as it does not place any special restrictions on the inputs, which was the case with CC3. Also the algorithm uses an input model very similar to the traditional model, in terms of inputs and outputs. Hence the users may find it very easy to switch between the traditional neural network style and the network proposed in this paper. The algorithm sets up a neural network. The weights are assigned by looking at the inputs. In testing, the inputs are provided and the most probable output is calculated. The neural network uses a single hidden layer. The best neurons of the hidden layer are invoked at every input. This algorithm was trained on some points of a 2 color picture. When we tried to reproduce it, the results showed the algorithm was efficient and accurate.