A generalization of Shannon's information theory

A generalized information theory is proposed as a natural extension of Shan non's information theory. It proposes that information comes from forecasts . The more precise and the more unexpected a forecast is, the more informat ion it conveys. If subjective forecast always conforms with objective facts then the generalized information measure will be equivalent to Shannon's i nformation measure. The generalized communication model is consistent with Popper's model of knowledge evolution. The mathematical foundations of the new information theory, the generalized communication model, information me asures for semantic information and sensory information, and the coding mea nings of generalized entropy and generalized mutual information are introdu ced. Assessments and optimizations of pattern recognition, predictions, and detection with the generalized information criterion are discussed. For ec onomization of communication, a revised version of rate-distortion theory: rate-of-keeping-precision theory, which is a theory for datum compression a nd also a theory for matching an objective channels with the subjective und erstanding of information receivers, is proposed. Applications include stoc k market forecasting and video image presentation.