| 摘要 |
A predictive dialing system having a computer connected to a telephone switch stores a group of call records in its internal storage. Each call record contains a group of input parameters, including the date, the time, and one or more workload factors. Workload factors can indicate the number of pending calls, the number of available operators, the average idle time, the connection delay, the completion rate, and the nuisance call rate, among other things. Ih the preferred embodiment, each call record also contains a dial action, which indicates whether a call was initiated or not. These call records are analyzed by a neural network to determine a relationship between the input parameters and the dial action stored in each call record. This analysis is done as part of the training process for the neural network. After this relationship is determined, the computer system sends a current group of input parameters to the neural network, and, based on the analysis of the previous call records, the neural network determines whether a call should be initiated or not. The neural network bases its decision on the complex relationship it has learned from its training data -- perhaps several thousand call records spanning several days, months, or even years. The neural network is able to automatically adjust -- in a look ahead, proactive manner -- for slow and fast periods of the day, week, month, and year.
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