System identification: theory for the user
System identification: theory for the user
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural control of turbogenerator systems
Automatica (Journal of IFAC)
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Design of an adaptive neural network based power system stabilizer
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
IEEE Transactions on Neural Networks
A simple procedure for pruning back-propagation trained neural networks
IEEE Transactions on Neural Networks
Agent-based simulation of competitive and collaborative mechanisms for mobile service chains
Information Sciences: an International Journal
Improving artificial neural networks based on hybrid genetic algorithms
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Measuring the weight of egg with image processing and ANFIS model
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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This paper presents two different power system stabilizers (PSSs) which are designed making use of neural fuzzy network and genetic algorithms (GAs). In both cases, GAs tune a conventional PSS on different operating conditions and then, the relationship between these points and the PSS parameters is learned by the ANFIS. ANFIS will select the PSS parameters based on machine loading conditions. The first stabilizer is adjusted minimizing an objective function based on ITAE index, while second stabilizer is adjusted minimizing an objective function based on pole-placement technique. The proposed stabilizers have been tested by performing simulations of the overall nonlinear system. Preliminary experimental results are shown.