| 摘要 |
A classifier for signal classification based on a plurality of classification signals can be generated in three phases: a training phase, an orchestration phase, and a final classifier phase. In the training phase, genetic programming techniques are used to evolve a population of classification programs to produce a series of groups within the population, each of which is the best able to classify one type of input signal from all other signals. In the orchestration phase, the best programs from each group are selected and placed in a hierarchy of systems, wherein each system contains the best programs that are able to classify one type of input signal from all other signals. Default weights are assigned to each system and each program within a system. Training signals are input into the selected programs, output values for each program and system are generated, and the weights associated with the programs and systems are adjusted. The final classifier is the final hierarchy of systems of selected programs used for the classification of signals with unknown labels. The genetic programming techniques used in the training phase can also be applied to populations of computer programs other than signal classification programs such as operator programs.
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