Use of adaptive resolution for better CMAC learning

The quantization of the input space affects the performance of cerebellar model arithmetic computer (CMAC)-based systems. The conventional CMAC uses equal-size quantization without considering the variation of the target function in different areas. The new scheme presented is capable of adaptively changing the input quantization through the use of the so-called mapping functions. For a fixed number of blocks and elements, larger blocks and elements are used for the areas with less variation in control signal. Memory is efficiently used. Through the repeated learning and mapping function updating, better learning results can be achieved. Simulation results for a single-variable case are encouraging