Abstract
In this work we propose an immune approach for learning neuro-fuzzy 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.
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Korablev, N., Sorokina, I. (2011). Immune Approach for Neuro-Fuzzy Systems Learning Using Multiantibody Model. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_34
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DOI: https://doi.org/10.1007/978-3-642-22371-6_34
Publisher Name: Springer, Berlin, Heidelberg
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