Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Computational Intelligence Techniques for Short-Term Electric Load Forecasting
Journal of Intelligent and Robotic Systems
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
Integrating actuator fault and wheel slippage detections within FDI framework
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Distributed sensor analysis for fault detection in tightly-coupled multi-robot team tasks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Neural networks with asymmetric activation function for function approximation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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In this paper we investigate the use of the multi-layer perceptron (MLP) for system modelling. A new sigmoidal activation function is introduced and the study is focused at the utilization of this function on a MLP that performs modelling of dynamic, discrete time systems. The role of the activation function in the training process is investigated analytically, and it is proven that the shape of the activation function and it's derivative can affect the training outcome. The method is simulated at a well known benchmark, namely the three tank system, and is incorporated in a Fault Detection and Identification (FDI) method, also applied and simulated at the three tank system. Finally, a comparison is made with an approach that utilizes local model neural networks for system modeling.