Adaptive and interchangeable neural networks utilizing predicting information

Abstract

Methods and systems that allow neural network systems to maintain or increase operational accuracy while being able to operate in various settings that may include predicting information. A set of training data is collected over each of at least two different settings. Each setting has a set of characteristics. Examples of setting characteristic types can be time, geographical location, and/or weather condition. Each set of training data is used to train a neural network resulting in a set of coefficients. For each setting, the setting characteristics are associated with the corresponding neural network having the resulting coefficients and neural network structure. A neural network, having the coefficients and neural network structure resulted after training using the training data collected over a setting, would yield optimal results when operated in/under the setting. A database management system can store information relating to the setting characteristics, neural network coefficients, and/or neural network structures.