Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1998-04-17
2001-09-11
Davis, George B. (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C706S019000, C706S021000
Reexamination Certificate
active
06289328
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a system and method for self-training a neural network classifier with automated outlier detection. More particularly, the present invention relates to a chemical sensor pattern recognition system and method for detecting and identifying the presence of chemical agents using a self-training neural network classifier employing a probabilistic neural network with a built in outlier rejection algorithm and a mechanism to reduce the size of the probabilistic neural network.
2. Description of the Related Art
In the industrial and the military environments a need has existed for a mechanism to identify a wide variety of chemical substances on a real-time basis. These substances often include compounds which are extremely dangerous. In the industrial environment these substances may include known carcinogens and other toxins. In the military environment these substances would include blistering agents such as mustard gas and neurotoxins such as nerve gas. Therefore, it is critical for the safety of personal to quickly and accurately detect and alert employees and troops when such substances are present. Just as critical a function is the avoidance of issuing false alarms by any chemical detection apparatus.
FIG. 1
is a diagram showing a configuration of a chemical detection apparatus known in the prior art which includes a sensor
10
and a pattern recognition unit
20
. The pattern recognition unit
20
would include a computer system and software to analyze data received from the sensor
10
in order to identify the substance detected.
Referring to
FIG. 1
, traditional chemical detection methods have relied on the inherent selectivity of the sensor
10
to provide the pattern recognition unit
20
with the necessary information required to determine the presence or absence of target analytes. Advancements in chemical sensor technology have allowed the chemical detection apparatus shown in
FIG. 1
to move from the laboratory to the field.
However, field measurements offer additional challenges not seen in laboratory or controlled environments. The detection of target analytes may be required in the presence of large concentrations of interfering species. The ideal chemical sensor
10
responds only to the targeted analyte(s). However, many sensor technologies, such as polymer-coated surface acoustic wave (SAW) chemical sensors, cannot achieve this measure of selectivity. Progress has been made and researchers have been able to overcome this potential drawback by utilizing arrays of partially selective sensors for sensor
10
. Pattern recognition algorithms, in the pattern recognition unit
20
, are then employed to interpret the sensor signals to provide an automated decision concerning the presence or absence of the targeted analyte(s). This approach has been employed successfully for semi-conducting gas oxide sensors, Taguchi gas sensors, MOSFET sensors, electrochemical sensors, and polymer-coated SAWs for the analysis of both liquid and gas phase species.
The underlying foundations for applying pattern recognition methods to chemical sensor arrays
10
are that the sensor signals numerically encode chemical information (i.e., a chemical signature) about the target analytes and the interfering species. In addition, pattern recognition methods assume that sufficient differences in the chemical signatures for the target analyte(s) and the interfering species exists for the methods to exploit, and that the differences remain consistent over time. For chemical sensor array pattern recognition, the responses for the number of sensors (represented by m) in the array form an m-dimensional vector (“vector pattern”) in the data space. Recognition of the signature of the target compound(s) (analyte(s)) is based on the clustering of the patterns in the m-dimensional space. Analytes that have similar chemical features will cluster near each other in the data space, which allows them to be distinguished from other compounds mathematically.
FIG. 2
is a diagram showing a pattern space comprising a sensor array with three sensors (
1
,
2
,
3
) and three chemical analytes (A, B, C). Since three sensors are used, the data space is a three dimensional data space. The three chemical analytes (A, B, C) form three different and easily distinguishable clusters of patterns (chemical signatures) in the three dimensional space. However, when attempting to detect chemicals in an environment outside the laboratory, frequently chemicals that closely chemically match the chemical to be identified are present. The closely related chemical is referred to as an interfering species and creates a pattern which partly overlaps with the cluster of the chemical to be detected.
In supervised pattern recognition methods, training patterns (i.e., chemical signatures) from known analytes and potential interfering species representative of the environment the sensors being deployed are used to develop classification rules by the pattern recognition unit
20
. These classification rules are used to predict the classification of future sensor array data. The training patterns are obtained by exposing the sensor array to both the target analyte(s) and potential interfering analytes under a wide variety of conditions (e.g., varying concentrations and environments). The potential outcomes of the measurement (e.g., the presence or absence of the target analyte(s)) are considered the data classes. The number of data classes is application specific.
Supervised pattern recognition algorithms used in pattern recognition unit
20
are known in the art and used to analyze chemical sensor
10
array data. The two most popular pattern recognition approaches are linear discriminant analysis (LDA) and artificial neural networks (ANN). LDA is computationally simpler and easier to train than an ANN, but has trouble with multi-modal and overlapping class distributions. ANNs have become the de facto standard for chemical sensor pattern recognition due to the increasing power of personal computers and their inherent advantages in modeling complex data spaces.
The typical ANN for chemical sensor array pattern recognition uses the back-propagation (BP) method for learning the classification rules. The conventional ANN comprises of an input layer, one or two hidden layers, and an output layer of neurons. A neuron is simply a processing unit that outputs a linear or nonlinear transformation of its inputs (i.e., a weighted sum). For chemical sensor arrays, the neurons, as a group, serve to map the input pattern vectors to the desired outputs (data class). Using BP, the weights and biases associated with the neurons are modified to minimize the mapping error (i.e., the training set classification error). Upon repeated presentation of the training patterns to the ANN, the weights and biases of the neurons become stable and the ANN is said to be trained. The weights and biases for the neurons are then downloaded to the chemical sensor system for use in predicting the data classification of new sensor signals.
Despite their popularity, the BP-ANN methodology has at least five major disadvantages for application to chemical sensor arrays.
First, no known method exists for determining the optimal number of hidden layers and hidden layer neurons (i.e., the neural topology). This results in having to train many ANNs before finding one that is best for the application at hand.
Second, the iterative BP training algorithm is extremely slow, sometimes requiring several thousand presentations of the training patterns before convergence occurs. Other ANN training methods, such as Levenberg-Marquardt and QuickProp method, claim to achieve faster convergence, but their use is not widespread. Also, any learning algorithm based on incremental modifications to the weights and biases of the neurons runs the risk of falling prey to false minima and thereby requiring multiple training runs which further slow the process.
Third, the theoretical operation of how the ANN is able t
Davis George B.
Karasek John J.
Stockstil Charles J.
The United States of America as represented by the Secretary of
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