Alakhras, M.N.Y., 2005. Neural network-based fuzzy inference system for exchange rate prediction. Journal of Computer Science, 112-120.M. N. Alakhras, "Neural network-based fuzzy inference system for exchange rate prediction," Journal of Computer Science, pp. 112-120, 2005....
J.R. Jang, Self-Learning Fuzzy Controllers Based on Temporal Back Propagation, IEEE Transactions on Neural Networks, 3, 714–723, 1992. Article Google Scholar J.M. Keller, R. Krishnapuram, and F.C. Rhee, Evidence Aggregation Networks for Fuzzy Logic Inference, IEEE Transactions on Neural ...
In their simplest form, a fuzzy neural network can be viewed as a three-layer feedforward network, with a fuzzy input layer (fuzzification), a hidden layer containing the fuzzy rules, and a final fuzzy output layer (defuzzification).
An alternative approach, Adaptive Network based Fuzzy Inference System (ANFIS), is proposed to predict the pre-evacuation behavior of peoples, which is an artificial neural network (ANN) based predictive model and integrates fuzzy logic (if-then rules) and neural network (based on back propagation...
In this paper, a neural network realization of the Takagi-Sugeno-Kang fuzzy inference model is considered. The basic idea of using neural networks to realize the TSK model is to implement the membership functions in the preconditions as well as the inference functions in the consequences by prope...
First, the name “fuzzy neural network” implies a likeness between fuzzy neurons and biological neurons—the natural elements for information processing. This clear assumption is frequently left out of consideration, especially in the case of fuzzy logic-based FNNs. Namely, some of the so-called...
Asian Network for Scientific InformationJournal of Environmental Science & TechnologyMpallas L, Tzimopoulos C, Evangelides C (2011) Comparison between Neural Networks and Adaptive Neuro-fuzzy Inference System in Modeling Lake Kerkini Water Level Fluctuation Lake Manegement using Artificial Intelligence. J...
In this paper, both feed-forward artificial neural networks (ANN) and adaptive network-based fuzzy inference systems (ANFIS) have been applied to switched circuits and systems. Then their performances have been compared in this contribution by developed simulation programs. It has been shown that ...
Fuzzy inference neural networks, which combine fuzzy systems and artificial neural networks, are presented. The clustering algorithm is used for the sample data and the fuzzy rule base is created. The fuzzy inference neural network is applied to solve the medical diagnosis problem....
A recurrent, neural network-based fuzzy logic system includes neurons in a rule base layer which each have a recurrent architecture with an output-to-input feedback path including a time delay element and a neural weight. Further included is a neural network-based, fuzzy logic finite state mach...