Abstract: A framework for use with object-oriented programming systems includes one or more objects of a class called “Socket” that receive and process packets of work. As various applications execute, they require servicing of tasks, which correspond to the packets of work mentioned above. The packets of work are represented in the framework by objects of a class called “WorkUnit”. When a WorkUnit object is generated by an application, it in turn generates an object of a class called “Retriever” which is associated with the appropriate Socket object needed for servicing the WorkUnit. The Retriever object retrieves the Socket object to service the WorkUnit from an object of a class called “SessionInfo”. The SessionInfo object is a single object for maintaining registries of Socket objects and objects of a class called “ApplicationInfo”.

Abstract: A neural network development utility assists a developer in generating one or more filters for data to be input to or output from a neural network. A filter is a device which translates data in accordance with a data transformation definition contained in a translate template. Source data for the neural network may be expressed in any arbitrary combination of symbolic or numeric fields in a data base. The developer selects those fields to be used from an interactive menu. The utility scans the selected field entries in the source data base to identify the logical type of each field, and creates a default translate template based on this scan. Numeric data is automatically scaled. The developer may use the default template, or edit it from an interactive editor. When editing the template, the developer may select from a menu of commonly used neural network data formats, and from a menu of commonly used primitive mathematical operations.

Abstract: An enhanced neural network shell for application programs is disclosed. The user is prompted to enter in non-technical information about the specific problem type that the user wants solved by a neural network. The user also is prompted to indicate the input data usage information to the neural network. Based on this information, the neural network shell creates a neural network data structure by automatically selecting an appropriate neural network model and automatically generating an appropriate number of inputs, outputs, and/or other model-specific parameters for the selected neural network model. The user is no longer required to have expertise in neural network technology to create a neural network data structure.

Abstract: A neural network development utility assists a developer in generating one or more filters for data to be input to or output from a neural network. A filter is a device which translates data in accordance with a data transformation definition contained in a translate template. Source data for the neural network may be expressed in any arbitrary combination of symbolic or numeric fields in a data base. The developer selects those fields to be used from an interactive menu. The utility scans the selected field entries in the source data base to identify the logical type of each field, and creates a default translate template based on this scan. Numeric data is automatically scaled. The developer may use the default template, or edit it from an interactive editor. When editing the template, the developer may select from a menu of commonly used neural network data formats, and from a menu of commonly used primitive mathematical operations.

Abstract: An enhanced neural network shell for application programs is disclosed. The user is prompted to enter in non-technical information about the specific problem type that the user wants solved by a neural network. The user also is prompted to indicate the input data usage information to the neural network. Based on this information, the neural network shell creates a neural network data structure by automatically selecting an appropriate neural network model and automatically generating an appropriate number of inputs, outputs, and/or other model-specific parameters for the selected neural network model. The user is no longer required to have expertise in neural network technology to create a neural network data structure.