Abstract
Article history: Received 10 September 2010 Received in revised form 4 January 2011 Accepted 6 January 2011 Available online 6 January 2011 At the computational point of view, a fuzzy system has a layered structure, similar to an artificial neural network (ANN) of the radial basis function type. ANN learning algorithms can be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling approach has preference to explain solutions over completely black-box models, such as ANN. In this paper, we implement the design of experiment (DOE) technique to identify the significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS) for stock price prediction. © 2011 Growing Science Ltd. All rights reserved
Highlights
Fuzzy systems and neural networks (NN) are considered as two most widely used techniques in intelligent systems
The aim of this paper is to extend their work by considering more factors in experimental design and to test their influence on the behavior adaptive neuro-fuzzy inference systems (ANFIS)
Starting with the popularity and prevalent use of the neuro-fuzzy systems, specially ANFIS, we explained the inherent difficulty of its designing and parameter setting process
Summary
Fuzzy systems and neural networks (NN) are considered as two most widely used techniques in intelligent systems. NN are adaptive systems that can be trained and tuned from a set of input-output data set. Adaptive neuro-fuzzy inference systems (ANFIS) represent a neural network approach to the design of fuzzy inference systems (Jang, 1993). ANFIS networks have been widely considered in the technical literature and successfully applied to classification tasks, rule-based expert systems, prediction of time series, and so on. ANFIS is a fuzzy inference system that can be trained to model the collection of input-output data. This network makes use of a supervised learning algorithm to determine a nonlinear relationship among inputs and output. According to Kosko (1994), an ANFIS network is suited to solve function approximation problems in several engineering fields
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Industrial Engineering Computations
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
