Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Inductive Learning of Mutation Step-Size in Evolutionary Parameter Optimization
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Designing ensembles of fuzzy classification systems: an immune-inspired approach
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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In this work we propose an immune approach for learning neurofuzzy systems, namely Adaptive-Network-based Fuzzy Inference System (ANFIS). ANFIS is proved to be universal approximator of nonlinear functions. But in case of great number of input variables ANFIS structure grows essentially and the dimensionality of learning task becomes a problem. Existing methods of ANFIS learning allow only to identify parameters of ANFIS without modifying its structure. We propose an immune approach for ANFIS learning based on clonal selection and immune network theories. It allows not only to identify ANFIS parameters but also to reduce number of neurons in hidden layers of ANFIS. These tasks are performed simultaneously using the model of adaptive multiantibody.