System identification: theory for the user
System identification: theory for the user
Information-based objective functions for active data selection
Neural Computation
Constructive Backpropagation for Recurrent Networks
Neural Processing Letters
High-order and multilayer perceptron initialization
IEEE Transactions on Neural Networks
How initial conditions affect generalization performance in large networks
IEEE Transactions on Neural Networks
On the Kalman filtering method in neural network training and pruning
IEEE Transactions on Neural Networks
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Application of neural network for air-fuel ratio identification in spark ignition engine
International Journal of Computer Applications in Technology
Transient Air-Fuel Ratio Estimation in Spark Ignition Engine Using Recurrent Neural Networks
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
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The paper deals with the identification of recurrent neural networks (RNNs) for simulating the air-fuel ratio (AFR) dynamics into the intake manifold of a spark ignition (SI) engine. RNN are derived from the well-established static multi layer perceptron feedforward neural networks (MLPFF), that have been largely adopted for steady-state mapping of SI engines. The main contribution of this work is the development of a procedure that allows identifying a RNN-based AFR simulator with high generalization and limited training data set. The procedure has been tested by comparing RNN simulations with AFR transients generated using a nonlinear-dynamic engine model. The results show how training the network making use of inputs that are uncorrelated and distributed over the entire engine operating domain allows improving model generalization and reducing the experimental burden. Potential areas of application of the procedure developed can be either the use of RNN as virtual AFR sensors (e.g. engine or individual AFR prediction) or the implementation of RNN in the framework of model-based control architectures. rchitectures.