Research Article, J Electr Eng Electron Technol Vol: 4 Issue: 1
Neuro-Fuzzy Approach Towards Technoeconomic Forecasting
| Dolores De Groff1*, Mohammad Dabbas2 and Perambur Neelakanta1 | |
| 1Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA | |
| 2Department of Engineering, Broward College, Coconut Creek, Florida, USA | |
| Corresponding author : Dolores De Groff Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, Fl. 33431, USA Tel: +561.297.1261 E-mail: degroff@fau.edu |
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| Received: June 20, 2015 Accepted: September 02, 2015 Published: September 07, 2015 | |
| Citation: De Groff D, Dabbas M, Neelakanta P (2015) Neuro-Fuzzy Approach towards Technoeconomic Forecasting. J Electr Eng Electron Technol 4:1. doi:10.4172/2325-9833.1000116 |
Abstract
Proposed in this paper is a fuzzy inference engine (FIE) intended to ascertain ex ante forecast details on a dependent variable y, based on a set of ex post information gathered on y in technoeconomic contexts. The FIE constructed thereof conforms to an artificial neural network (ANN), and, the ANN outcome deduced yields the forecasting on the temporal evolution of y(t) in the ex ante time-frame (t) vis-à-vis a set of ex post data availed. The ex post data available is however, sparse and inadequate for robust forecasting. Therefore, its cardinality is first improved and sufficient number of such sets is obtained as pseudoreplicates via statistical bootstrapping. The test ANN then uses these pseudoreplicates as training inputs toward robust prediction/forecast schedules. Further, the pseudoreplicated sets are considered as overlapping and hence, fuzzy. Therefore, the test ANN adopted is relevant to a FIE realization. Real-world technoeconomic data set on ADSL sales-cum-facility details at a wire-center in a telecommunication company (telco) is used to test the efficacy of the FIE proposed and validate the forecasting method described.
