Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy logic and neurofuzzy applications explained
Fuzzy logic and neurofuzzy applications explained
A fuzzy neural network for rule acquiring on fuzzy control systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Deep combination of fuzzy inference and neural network in fuzzy inference software—FINEST
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Dynamic system identification via recurrent multilayer perceptrons
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive recurrent-neural-network motion controller for X-Y table in CNC Machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
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Conventional neuro-fuzzy systems cannot effectively cope with dynamic processes, such as the heating systems of the buildings, due to the feed forward network structure. To overcome this problem, the existing hybrid system is incorporated with a feedback loop so that it can model the dynamical behavior of the process. As a case study, this improved hybrid system is employed to build an inferential model that estimates the average air temperature in a building served by a forced-warm-air heating system. The results show that the inferential model based on this improved hybrid system is accurate and robust. The parameter identification and tuning process is effective. Compared with conventional hybrid neuro-fuzzy system, it can significantly improve the performance of the inferential models. The neuro-fuzzy model incorporated with the feed-back loop has been tested using experimental data and the worst monthly RMSE as 0.062^oC. This means that the inferential model can be employed to design better control schemes to improve indoor environmental quality and to save energy in buildings.