Method of and system for controlling learning in neural network

A system for controlling learning in a neural network, comprising:

• first storing means for storing a learning pattern data set;
• second storing means for storing a reference data set;
• third storing means for storing an evaluation data set;
• fourth storing means for storing a list of restoring methods;
• display means for displaying data inputted thereto;
• learning means for reading out a current learning pattern data of the learning pattern data set for a current problem from said first storing means and performing learning of the current problem in the neural network according to the read out learning pattern data;
• protraction determining means for reading a current reference data of the reference data set from said second storing means, storing, as an element of the evaluation data set in said third storing means, current evaluation data indicative of a learning state of a neural network and obtained during the learning, and determining whether or not there exists a possibility of protraction of the learning, in accordance with the read out current reference data, the obtained evaluation data and a predetermined judging condition set;
• selection auxiliary means for respectively reading the current evaluation data and the list of restoring methods from said third and fourth storing means when it is determined that there exists the possibility of the learning protraction and outputting the read out current evaluation data and the read out list of restoring methods to said display means for selection of a particular restoring method by a user;
• learning control means for controlling said learning means to perform the learning of the current problem in accordance with the particular restoring method designated by the user, when it is determined that there exists the possibility of the learning protraction;
• and
• fifth storing means for storing a case data set indicating past cases, the case data set including a particular restoring method adopted for a past learning that encountered a problem similar to the current problem, and a past evaluation data set employed in the past learning, wherein said selection auxiliary means further comprises means for storing in said fifth storing means, as new case data of the case data set, the selected restoring method and the evaluation data set.

A method of controlling learning on a current problem in a neural network, comprising the steps of:

• initiating the learning of the current problem in accordance with learning pattern data;
• judging whether or not there exists a possibility of protraction of the learning, in accordance with current evaluation data obtained during the learning, a reference data set, and a predetermined judging condition set, each current evaluation data representing a state of the learning in the neural network;
• displaying a selection auxiliary data and a list of restoring methods for the user to select the particular storing method when it is judged that there exists the possibility of the learning protraction;
• re-initiating the learning in accordance with the particular restoring method selected by the user;
• wherein the step of displaying includes displaying past evaluation data for each of past problems which is analogous to the current problem, a past learning data set for each of the past problems, and a new learning method for each of the past problems employed when it was judged that there existed a possibility of the learning protraction in learning each of the past problems, respectively.