Affiliations: [a] Institute of Command and Control Engineering, The Army Engineering University, Nanjing, China
| [b] Nursing College, Second Military Medical University, Shanghai, China
| [c] School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia UKM Bangi, Selangor, Malaysia
Correspondence:
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Corresponding authors. Yuanyuan Shen, Nursing College, Second Military Medical University, Shanghai, China. E-mail: [email protected] and Nasruddin Hassan, School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia. E-mail: [email protected].
Abstract: In the past, intelligent system often realized reasoning operation by interpolation method for one-dimensional sparse rule base, and could not analyze fuzzy reasoning of multi-dimensional sparse rule condition, which greatly improved the error and volatility of reasoning results. Therefore, a multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is proposed. Through the CMAC neural network, the influence weight of each variable is extracted. CMAC neural network is applied to train weights of multi-dimensional variables in multiple multi-dimensional fuzzy reasoning rules, and local correction weights are made, so that the weights of each modification are very few. After fast learning, the influence weights of the multi-dimensional variables on the reasoning result are obtained. A multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is applied to input the given neighboring rules into CMAC neural network, and the weights of the variables in the neighboring rules are obtained. According to the linear interpolation and the sequence of interpolation cardinal numbers, the influence weights of the variables in the observation value are determined. According to the linear interpolation reasoning method, a new fuzzy rule is constructed. Based on the approximation between the new fuzzy rules and the observed values, the similarity between the predicted values and the new fuzzy rules is constructed. The result of fuzzy inference is obtained according to the similarity. The experimental results show that the proposed algorithm has high reasoning precision and stability, and the practical application effect is good.
