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Furthermore, the PNN weights are updated algebraically, reducing the computational load of learning significantly. Simulation results for a Duffing oscillator and a fuzzy cognitive network illustrate the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.4018\/jalr.2011010101","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T12:04:25Z","timestamp":1319025865000},"page":"1-16","source":"Crossref","is-referenced-by-count":2,"title":["An Observer Approach for Deterministic Learning Using Patchy Neural Networks with Applications to Fuzzy Cognitive Networks"],"prefix":"10.4018","volume":"2","author":[{"given":"H. E.","family":"Psillakis","sequence":"first","affiliation":[{"name":"Technological and Educational Institute of Crete, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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