Applications of Feed-Forward Neural Networks with Error Backpropagation Algorithm and Non-Linear Methods in MATLAB

2010 ◽  
Author(s):  
Eleftherios Giovanis
Keyword(s):  
Neural Networks ◽  
Backpropagation Algorithm ◽  
Feed Forward ◽  
Non Linear ◽  
Feed Forward Neural Networks

scholarly journals Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing

2019 ◽  
Vol 9 (22) ◽  
pp. 4748 ◽  
Author(s):  
Umberto Michelucci ◽  
Francesca Venturini
Keyword(s):  
Neural Networks ◽  
Functional Dependence ◽  
Feed Forward ◽  
Multiple Parameters ◽  
Task Learning ◽  
Non Linear ◽  
Input Dataset ◽  
Feed Forward Neural Networks ◽  
Regression Problems ◽  
Multiple Variables

The classical approach to non-linear regression in physics is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterized by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built in the models to compensate for the difficulty of modeling things that are, by construction, difficult to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here, the MTL architecture allows predicting multiple parameters, the oxygen concentration and temperature, from a single set of measurements.

scholarly journals Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing

2019 ◽  
Author(s):  
Umberto Michelucci ◽  
Francesca Venturini
Keyword(s):  
Neural Networks ◽  
Functional Dependence ◽  
Feed Forward ◽  
Multiple Parameters ◽  
Task Learning ◽  
Non Linear ◽  
Input Dataset ◽  
Feed Forward Neural Networks ◽  
Regression Problems ◽  
Multiple Variables

The classical approach to non-linear regression in physics, is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then, using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterised by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built-in the models to compensate for the impossibility of modeling things that are, by construction, impossible to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here the MTL architecture allows predicting multiple parameters, the oxygen concentration and the temperature, from a single set of measurements.

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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