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Although, they are extensively used as forecasting model, they have certain limitations which are detrimental to system performance. The training time of the artificial neural network is affected by the complexity of the system, and moreover, they require a large amount of data for complex problems. The worl presented in this article deals with the application of the generalized neuron model for forecasting the electricity price. The generalized neuron model overcomes the limitation of the conventional ANN. The electricity price of the New South Wales electricity market is forecast to test the performance of the proposed model. The free parameters of the proposed model are trained using fuzzy tuned genetic algorithms to increase efficacy of the model.<\/jats:p>","DOI":"10.4018\/ijaci.2018070104","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T10:16:37Z","timestamp":1523528197000},"page":"44-56","source":"Crossref","is-referenced-by-count":6,"title":["Short Term Price Forecasting Using Adaptive Generalized Neuron Model"],"prefix":"10.4018","volume":"9","author":[{"given":"Nitin","family":"Singh","sequence":"first","affiliation":[{"name":"Motilal Nehru National Institute of Technology (MNNIT) Allahabad, Allahabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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