Temporal Rough Neural Network

Abstract

This paper describes a new temporal rough neural network which consists of a combination of rough set and temporal concept. Temporal factors are combined with the input of neural network. That is to say, input of Neural Network is function of time, so conventional neurons are reconstructed temporal neurons. Neurons of temporal rough neural network is temporal rough neurons, they use pairs of upper and lower bounds as values for input and output and as variability for time. In some practical situations, it is preferable to develop prediction models that use ranges as values for input and/or output variables and as variability for time. A need to provide tolerance these ranges is an example of such a situation. Inability to record precise values of the variables is another situation where ranges of values must be used. In the example used in this study, a number of input values are associated with a single value of the output variable and with time. Hence, it seems appropriate to represent the input values as ranges. Temporal rough neural network can depict these situations and provide a better solution.