Abstract
This paper discusses the use of a novel model of neural networks, the generalized neural network model, to build the primitives for an adaptive compression system. This model adds to the today's connectionist model paradigms to include the behave-act, evolve-learn, and behave-control functions of neural networks, which allow the definition of connectionist systems that overcome the drawbacks of previous feedforward neural network-based compression systems. The approach yields a compression system that surpasses known compression algorithms in three main aspects: very high compression rate with a low introduced distortion, ability to tackle a broad set of data, and feasibility for on-line real-time compression.
