Hybrid learning-based neuro-fuzzy inference system: a new approach for system modeling

Authors:
K. -H. Cheng
Affiliations:
Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, Taiwan, Republic of China
Venue:
International Journal of Systems Science
Year:
2008

Citing 23
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks
A new approach for finding the global minimum of error function* of neural networks

Neural Networks
A function estimation approach to sequential learning with neural networks

Neural Computation
Fuzzy engineering

Fuzzy engineering
Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction

Fuzzy Sets and Systems
A sequential learning scheme for function approximation using minimal radial basis function neural networks

Neural Computation
Neuro-fuzzy systems for function approximation

Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Fuzzy control of unknown multiple-input—multiple-output plants

Fuzzy Sets and Systems
Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition

Fuzzy Sets and Systems
Computer Vision and Fuzzy-Neural Systems

Computer Vision and Fuzzy-Neural Systems
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Dynamic fuzzy neural networks-a novel approach to functionapproximation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A GA-based method for constructing fuzzy systems directly from numerical data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy modeling

IEEE Transactions on Fuzzy Systems
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants

IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning

IEEE Transactions on Fuzzy Systems
Pseudoerror-based self-organizing neuro-fuzzy system

IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework

IEEE Transactions on Neural Networks
A new clustering technique for function approximation

IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)

IEEE Transactions on Neural Networks
Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)

IEEE Transactions on Neural Networks

Development of a rule selection mechanism by using neuro-fuzzy methodology for structural vibration suppression

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this article, a hybrid learning neuro-fuzzy inference system (HLNFIS) with a new inference mechanism is proposed for system modeling. In the HLNFIS, the incoming signal is fuzzified by the proposed improved Gaussian membership function (IGMF), which is derived from two standard Gaussian functions. With the premise construction with IGMFs, the system inference ability can be upgraded. The fuzzy inference processor, which involves both numerical and linguistic reasoning, is introduced in rule base construction. For effective parameter learning, the hybrid algorithm of random optimization (RO) and least square estimation (LSE) is exploited, where the premise and the consequence parameters of are updated by RO and LSE, respectively. To validate the feasibility and the potential of the proposed approach, three examples of system modeling are conducted. Through experimental results and comparisons the proposed HLNFIS shows excellent performance for complex modeling.