Non-linear projection to latent structures revisited (the neural network PLS algorithm)

The contribution deals with the prediction of crop yield levels, using an artificial intelligence approach, namely a multi-layer neural network model. Subsequently, we are contrasting this approach with several non-linear regression models, the usefulness of which has been tested and published several times in the specialized periodicals. The main stress is placed on judging the accuracy of the individual methods and of the implementation. A neural network simulation device is that which enables the user to set an adequate configuration of the neural network vis á vis the required task. The conclusions can be generalized for other tasks of a similar nature, especially for the tasks of a non-linear character, where the benefits of this method increase.

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Nonlinearities in the neural network and their significance (negative non-linear feedback)

Nonlinear Analysis Modelling and Control ◽

10.15388/na.1998.2.0.15286 ◽

1998 ◽

Vol 2 ◽

pp. 59-68

Author(s):

Dobilas Kirvelis

Keyword(s):

Neural Network ◽

Linear Feedback ◽

The Neural Network ◽

Non Linear

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Modelling of a Novel Gas Strut Using Neural Networks

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2004-59119 ◽

2004 ◽

Cited By ~ 2

Author(s):

B. Gao ◽

J. Darling ◽

D. G. Tilley ◽

R. A. Williams ◽

A. Bean ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Mathematical Model ◽

Least Square ◽

Operation Conditions ◽

The Neural Network ◽

Wide Range ◽

Non Linear ◽

Vehicle Suspension System ◽

And Control

The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal ‘black-box’ models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.

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Modelling methane-air turbulent diffusion flame in a gas turbine combustor with artifical neural network

The Aeronautical Journal ◽

10.1017/s0001924000003195 ◽

2009 ◽

Vol 113 (1146) ◽

pp. 541-547

Author(s):

N. S. Mehdizadeh ◽

P. Sinaei

Keyword(s):

Neural Network ◽

Gas Turbine ◽

Network Architecture ◽

Turbulent Flame ◽

Ann Model ◽

Turbulent Diffusion Flame ◽

Air Jet ◽

The Neural Network ◽

Non Linear ◽

Non Linear System

Abstract The present paper reports a way of using an artificial neural network (ANN) for modelling methane-air jet diffusion turbulent flame characteristics, such as temperature and chemical species mass fractions in a gas turbine combustion chamber. Since the neural network needs sets of examples to adapt its synaptic weights in the training phase, we used pre-assumed probability density function (PDF) method and considered chemical equilibrium chemistry model to compute the flame characteristics for generating the examples of input-output data sets. In this approach, flow and mixing field results are presented with a non-linear first order k-ε model. The turbulence model is applied in combination with preassumed β-PDF modelling for turbulence-chemistry interaction. The training algorithm for the neural network is based on a back-propagation supervised learning procedure, and the feed-forward multilayer network is incorporated as neural network architecture. The ability of ANN model to represent a highly non-linear system, such as a turbulent non-premixed flame is illustrated, and it can be summarized that the results of modelling of the combustion characteristics using ANN model are satisfactory, and the CPU-time and memory savings encouraging.

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