This article compares a neural network-based controller, both local and global networks, with fuzzy associative memories (FAM) on a nonlinear problem. CMAC and FAM are chosen as representatives of local generalization networks. CMAC controller is trained off-line, therefore, it can response to the incoming input immediately. CMAC can interpolate its memory and give a reasonable control signal even the input has not been trained on. Backpropagation is picked as a representative of global generalization networks. All three systems are studied on a simple simulated control problem. This preliminary research will be adapted later to control the laser cutting machine. A performance measure that depends on the transient response and the steady state response of the controlled system is used. The results indicate that CMAC and FAM are comparable
A. Thammano and C. H. Dagli, "A Comparison of FAM and CMAC for Nonlinear Control," Proceedings of the 3rd IEEE Conference on Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence, Institute of Electrical and Electronics Engineers (IEEE), Jan 1994.
The definitive version is available at http://dx.doi.org/10.1109/FUZZY.1994.343925
3rd IEEE Conference on Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence
Engineering Management and Systems Engineering
Keywords and Phrases
CMAC; Backpropagation; Cerebellar Model Arithmetic Computer; Content-Addressable Storage; Fuzzy Associative Memories; Fuzzy Control; Local Generalization Networks; Neural Nets; Neural Network-Based Controller; Nonlinear Control; Nonlinear Control Systems; Steady State Response; Transient Response
Article - Conference proceedings
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