HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS

DOI10.1142/S0218488502001478zbMath1075.93020OpenAlexW2052138683MaRDI QIDQ4824616

Witold Pedrycz, Dongwon Kim, Sung-Kwun Oh

Publication date: 1 November 2004

Published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218488502001478

zbMATH Keywords

hybrid architecturetime series datafuzzy rule-based computinggroup method of data handling (GMDH)polynomial neural networks (PNN)fuzzy polynomial neuron (FPN)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (3)

Genetically optimized hybrid fuzzy set based polynomial neural networks ⋮ Relation-based neurofuzzy networks with evolutionary data granulation ⋮ The design of self-organizing neural networks based on PNS and FPNs with the aid of genetic optimization and extended GMDH method

Cites Work

• An identification algorithm in fuzzy relational systems
• The evaluation of fuzzy models derived from experimental data
• Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems
• Multilayer feedforward networks are universal approximators
• Predicting a chaotic time series using a fuzzy neural network
• Fuzzy Model Identification and Self-Learning for Dynamic Systems
• Oscillation and Chaos in Physiological Control Systems

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