Abstract
A simple yet effective method to reduce the dimensions of the input variables and is adaptive to various users for intelligent controllers is proposed. The method has been developed specifically to address the challenge due to fuzziness in the system inputs, especially when studying the relationship of a large mapping between input variables and system response outputs. The proposed method exploits the principal components analysis to reduce the number of inputs and uses a fuzzy c-means technique to cluster them. The objective is to extract significant principal components for adaptive neural fuzzy inference systems (ANFIS) learning. The method has been applied to a robotic walker system for elderly movement assistance. Experimental results demonstrate the feasibility of the proposed method.
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