A neural network´s learning algorithm based on interval optimization

Artificial neural networks provide a neurocomputing approach for solving complex problems that might otherwise not have a tractable solution. Neural networks are usually trained using local gradient-based procedures. Such methods are frequently find sub-optimal solutions being trapped in local minima. And It will be difficult for the traditional neural network to solve such problems where the input/output data sets used to train a neural network may not be hundred percent precise but within certain range. A learning algorithm based on interval optimization is presented in this paper. The above disadvantages of the traditional learning algorithm can be settled by using this method.