This is achieved within the framework of the anticipatory paradigm. The basic assumption of the anticipatory paradigm is that a complex system can modify its behavior on the basis of present as well as anticipated future states. The complex systems contemplated in this research use measures of performance to represent current as well as anticipated states in such a manner that decisions about change of state are related to a search for maximizing the performance associated with a state variable.
Anticipated performance is computed on the basis of predictions made by pre-trained neural networks.
A fuzzified Bayes formula is used as the computational link between present and future states. Algorithms, Design, Performance The Digital Library is published by the Association for Computing Machinery.