On the training of a multi-resolution CMAC neural network
Alberico Menozzi, Mo–Yuen Chow · 2002
Several artificial neural network architectures have been proposed to solve problems in control systems and system identification. However, not all neural network structures are equally suitable for real-time adaptive situations. Lattice-based Associative Memory Networks (AMNs) have several properties that are attractive for real-time adaptive modeling and control. An example of a lattice-based AMN is the Cerebellar Model Articulation Controller (CMAC) neural network. A hierarchical multi-resolution lattice approach is proposed and investigated through experimentation as a possible approach to alleviate the main drawback of AMNs: the required storage is an exponential function of the number of inputs. Relevant issues are discussed and suggestions for future improvements are given.