Abstract: A neuroprocessing center executes a neuroprocessing using a neurocomputer. The neuroprocessing center is a public facility available for a user having a user terminal and executes the neuroprocessing as requested by the user. The user is given a result of the neuroprocessing. It is unnecessary for a user to have a computer implementing the neural network and anyone can participate in the profits of the neuroprocessing service. Examples of neuroprocessing service achieved by the neural network are graphic pattern generating services, character recognition services, sound synthesizing services, etc. A result of the neuroprocessing is effectively used by the user.

Abstract: A neural network (10) organizes the data items into a graphically oriented format by retrieving data items from a database (68) where each data item has a plurality of attributes. The neural network is organized (102) such that data items having similar attributes are assigned to neurons located closer together. The neurons of the neural network are matched (104) with the data items from the database and stored in a cross reference table. The cross reference table is displayed (106) on a computer screen (108) in a graphical format so that user visually relates the food items and sees the similarities and differences in their attribute data by the proximity of the data items to one another. The graphic format allows easy visual interpretation of the data items. For large databases, multiple neural networks (110, 112) can be organized hierarchically.

Abstract: A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22).

Abstract: Efficient expert systems for failure diagnosis often decisively increase the availability of a technical system by making available to a layman the knowledge of a diagnostic expert. The process attains the following objects: For each variant of a technical system, which is composed of modules, a knowledge base should be efficiently generated. An expert system should be able to quickly diagnose each variant. The process provides the following: In knowledge acquisition, knowledge modules are generated, namely a knowledge module for each type of real module, which occurs in at least one variant of the technical system. The knowledge module contains all diagnosis-relevant knowledge about the real module type in such a way that it is valid for each exemplar of the type.