{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:09Z","timestamp":1777704909845,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,8,11]]},"abstract":"<jats:p>This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks.<\/jats:p>","DOI":"10.3233\/jifs-202146","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T13:19:56Z","timestamp":1621603196000},"page":"499-516","source":"Crossref","is-referenced-by-count":1,"title":["Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks"],"prefix":"10.1177","volume":"41","author":[{"given":"Glender","family":"Br\u00e1s","sequence":"first","affiliation":[{"name":"Graduate Program in Mathematical and Computational Modeling, CEFET-MG - Federal Center of Technological Education of Minas Gerais, Av. 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