MATCH TRACKING STRATEGIES FOR FUZZY ARTMAP NEURAL NETWORKS

Abstract In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.

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Navigation Behaviors Based on Fuzzy ArtMap Neural Networks for Intelligent Autonomous Vehicles

Advances in Artificial Neural Systems ◽

10.1155/2011/523094 ◽

2011 ◽

Vol 2011 ◽

pp. 1-11

Author(s):

Amine Chohra ◽

Ouahiba Azouaoui

Keyword(s):

Neural Networks ◽

Decision Making ◽

Autonomous Vehicles ◽

Intelligent Systems ◽

Target Location ◽

Fuzzy Artmap ◽

Suggested Approach ◽

Hybrid Intelligent Systems ◽

Simplified Fuzzy Artmap ◽

Recognition Learning

The use of hybrid intelligent systems (HISs) is necessary to bring the behavior of intelligent autonomous vehicles (IAVs) near the human one in recognition, learning, adaptation, generalization, decision making, and action. First, the necessity of HIS and some navigation approaches based on fuzzy ArtMap neural networks (FAMNNs) are discussed. Indeed, such approaches can provide IAV with more autonomy, intelligence, and real-time processing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, after supervised fast stable learning: simplified fuzzy ArtMap (SFAM), to recognize both target-location and obstacle-avoidance situations using FAMNN1 and FAMNN2, respectively. Afterwards, the decision making and action consist of two association stages, carried out by reinforcement trial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards 30∘, 60∘, 90∘, 120∘, and 150∘. Third, simulation results display the ability of the FAMNN-based approach to provide IAV with intelligent behaviors allowing to intelligently navigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how its robustness would be if implemented on real vehicle, is given.

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