Credit-assignment-based parallel ensemble CMAC and its applications in modeling
基于信度分配的并行集成CMAC及其在建模中的应用

Albus CMAC (cerebella-model-articulation-controller) is a neural network that simulates the structure of the human cerebella and performs the articulation controller. Although it has a large memory capability and is capable of output generalization, Albus CMAC is still hard to meet the requirements of rapidity for online learning. To solve the conflict between the accuracy and memory capability of Albus CMAC, we introduce the concept of credit assignment and propose the parallel ensemble CMAC based on credit assignment. A large-scale network is separated into several subnetworks; these sub-networks are trained synchronously, and then are combined. It greatly improves the computational efficiency. In simulating the model of the complex nonlinear function, results show that the proposed scheme improves the generalization capability of the system model and raises the convergence rate of the improved arithmetic. Finally, how the learning parameter and the generalized parameter influence the effect of online learning of this neural network is discussed.