First choices of network composition and the topology.

First use of neural networks for
automotive application can be traced back to early 90s. In 1991, Marko tested
various neural classifiers for online diagnosis of engine control defects
(misfires) and proposed a direct control by inverse neural model of an active
suspension system 1. In 2, Puskorius and
Feldkamp, summarizing one decade of research, proposed neural nets for various
sub-functions in engine control: AFR and idle speed control, misfire detection,
catalyst monitoring, prediction of pollutant emissions. Indeed, since the
beginning of the 90s, neural approaches have been proposed by numerous authors,
for example:

 • Vehicle control: Anti-lock braking system
(ABS), active suspension, steering, speed control

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 • Engine modeling: Manifold pressure, air mass
flow, volumetric efficiency, Indicated pressure into cylinders, AFR,
start-of-combustion for Homogeneous Charge Compression Ignition (HCCI), torque
or power

 • Engine control: Idle speed control, AFR
control, transient fuel compensation (TFC), cylinder air charge control with
VVT, ignition timing control, throttle, turbocharger, EGR control, pollutants
reduction

 • Engine diagnosis: Misfire and knock
detection, spark voltage vector recognition systems

The works are too numerous to be
referenced here. Nevertheless, the reader can consult the publications 3, 4, 5, 6, 7 and the references
therein, for an overview.

 

1.    Architecture
choices of neural networks

2.1 Introduction
of architectures

The choice of network architecture
is dependent on the problem. Classification, linear or non-linear problems,
with or without underlying system dynamics guides the choices of network
composition and the topology. In general it can be distinguished between three
types of networks:

• Single-Feedforward Networks (SLFN)

• Multi-Layer Feedforward Networks
(MLFN)

• Recurrent Networks (RNN).

Where the single
feedforward network describes a simple mapping network it can be used in
classification or for mapping of simple input output functionality. It is
defined through a single layer of neurons. Hence, the knowledge storage
capacity is restricted and only simple logic relations can be mapped. An
extension of this is the multi-layer feedforward network, also found as
multi-layer perceptron. This network architecture is defined through a minimum
of one hidden layer of neurons. The number of hidden layers can be increased
dependent on the problem. However, literature
states (reference) that a multi-layer perceptron with three hidden layers is
sufficient to map every continuous function by adding a certain number of
neurons to meet required complexity. However, big growing networks can be
ill-posed for overtraining and be difficult to implement in real-time
applications. Therefore, recurrent structures of networks are in place that
will accommodate the underlying output dynamics, a feature that is of
particular interest with engine applications. In turbocharged combustion engines
intake and exhaust shows related dynamics through the turbine and compressor
connection. Those dynamics can be taken into consideration with output
recurrent network structures.

The automotive sector has applied neural networks
models in several different cases. Their main implementation is seen in control
design in the area of engine operation. Hence, in engine development neural
networks are used for control problems such as fuel injection, output
performance or speed 8. In addition, advanced
control strategies as variable turbine geometry (VGT), exhaust gas
recirculation (EGR) or variable valve timing (VVT) have been in the focus of
ANN modelling 9. Nevertheless, the
application is also used for virtual sensing such as emissions 10, 11 or as described in
Prokhorov 12 for misfire
detection, torque monitoring or tyre pressure change detection.

The combustion process itself has been investigated and
parameters been modelled with neural networks by different authors 13. Potenza developed a
model estimating Air-to-Fuel Ratio (AFR) or in-cylinder pressure and temperature
on the basis of crankshaft kinematics and its vibrations. In the work of He
combustion parameters and emissions are modelled under the consideration of
boost pressure and EGR.

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