Ann

DocumentToPDF trial version, to remove this mark, please register this software. ABSTRACT:

pattern matching, predict the outcome from a given set of inputs. The neural

This Report is an introduction to

network trains using a pattern file. In

Artificial Neural networks. It also deals

training, it converges on a proper set of

with an interesting application of neural

weights, or coefficients that lead from

network called “Electronic Nose”.

input

After

training

the

expression that is a function of inputs

being developed as systems for the

and weight coefficients, to obtain the

automated detection and classification of

output.

odors, vapors, and gases. An electronic of

output.

network, simply computes an arithmetic

Electronic/artificial noses are

nose is generally composed

/

a

1.1 A Neural Network

chemical sensing system (e.g., sensor array or spectrometer) and a pattern

An Artificial Neural Network

recognition system (e.g., artificial neural

(ANN) is an information processing

network). We are developing Electronic

paradigm that is inspired by the way

noses for the automated identification of

biological nervous systems, such as the

volatile chemicals for environmental and

brain, process information. The key

medical applications. In this paper, we

element of this paradigm is the novel

briefly describe an electronic nose, show

structure of the information processing

some results from a prototype electronic

system. It is composed of a large number

nose,

of

and

discuss

applications

of

highly

interconnected

processing

electronic noses in the environmental,

elements (neurones) working in unison

medical, and food industries.

to solve specific problems. ANNs, like people, learn by example. An ANN is

1.

Introduction

to

neural

configured for a specific application,

networks

such as pattern recognition or data classification,

DEFINITION:

through

a

learning

process. Learning in biological systems involves adjustments to the synaptic

Neural Networks are form of Artificial

Intelligence

that,

connections that

through

exist

between the

neurones. This is true of ANNs as well.

1

DocumentToPDF trial version, to remove this mark, please register this software. The first artificial neuron was produced

3. Real

Time

Operation:

ANN

in 1943 by the neurophysiologist Warren

computations may be carried out

McCulloch and the logician Walter Pits.

in parallel, and special hardware

But the technology available at that time

devices are being designed and

did not allow them to do too much.

manufactured

which

take

advantage of this capability. 1.2 Why to use neural networks? Neural

networks,

with

4. Fault Tolerance via Redundant Information

their

Coding:

Partial

destruction of a network leads to

remarkable ability to derive meaning

the corresponding degradation of

from complicated or imprecise data, can

performance.

be used to extract patterns and detect

network

trends that are too complex to be noticed

However,

capabilities

some

may

be

retained even with major network

by either humans or other computer

damage.

techniques. A trained neural network can be thought of as an "expert" in the

1.3

category of information it has been given

conventional computers

Neural

networks

versus

to analyze. This expert can then be used to

provide

projections

given

new

Neural networks take a different

situations of interest and answer "what

approach to problem solving than that of

if"questions.

conventional computers. Conventional

Other advantages include:

computers use an algorithmic approach i.e. the computer follows a set of

1. Adaptive learning: An ability to

instructions in order to solve a problem.

learn how to do tasks based on

Unless the specific steps that

the data given for training or

the

computer needs to follow are known the

initial experience.

computer cannot solve the problem. That

2. Self-Organization: An ANN can

restricts the problem solving capability

create its own organization or

of conventional computers to problems

representation of the information

that we already understand and know

it receives during learning time.

how to solve. But computers would be so much more useful if they could do

2

DocumentToPDF trial version, to remove this mark, please register this software. things that we don't exactly know how to

Neural

do.

networks

and

conventional algorithmic computers are not in competition but complement each Neural

networks

process

other. There are tasks are more suited to

information in a similar way the human

an algorithmic approach like arithmetic

brain does. The network is composed of

operations and tasks that are more suited

a large number of highly interconnected

to neural networks. Even more, a large

processing elements (neurons) working

number of tasks, require systems that use

in parallel to solve a specific problem.

a combination of the two approaches

Neural networks learn by example. They

(normally a conventional computer is

cannot be programmed to perform a

used to supervise the neural network) in

specific task. The examples must be

order to perform at maximum efficiency.

selected carefully otherwise useful time is wasted or even worse the network

Neural networks do not perform

might be functioning incorrectly. The

miracles. But if used sensibly they can

disadvantage is that because the network

produce some amazing results

finds out how to solve the problem by itself, its operation can be unpredictable. On the other hand, conventional computers use a cognitive approach to problem solving; the way the problem is to solved must be known and stated in small unambiguous instructions. These instructions are then converted to a high level language program and then into machine code that the computer can FIG: 1 NEURON

understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault.

3

DocumentToPDF trial version, to remove this mark, please register this software. 1.4 From Human Neurons to Artificial

to fire (or not), for particular input

Neurons

patterns.

We

conduct

neural

In the using mode, when a

networks by first trying to deduce the

taught input pattern is detected at the

essential features of neurons and their

input, its associated output becomes the

interconnections.

typically

current output. If the input pattern does

program a computer to simulate these

not belong in the taught list of input

features.

patterns, the firing rule is used to

We

However

these

then

because

our

knowledge of neurons is incomplete and

determine whether to fire or not.

our computing power is limited, our models

are

idealizations

necessarily of

real

gross

networks

of

neurons.

FIG: 3 A simple neuron Laboratory FIG: 2 Neuron model

multiprogram

(PNNL).

PNNL

national

is

a

laboratory

operated by Battelle Memorial Institute

1.5 A simple neuron

for the U.S. Department of Energy under Contract DE-AC06-76RLO 1830.

An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained

4

DocumentToPDF trial version, to remove this mark, please register this software. 2. ELECTRONIC NOSES AND

chemical

THEIR APPLICATIONS

automated fashion difficult.

data

in

an

identification is to build an array of

The two main components of an

sensors, where each sensor in the array is

electronic nose are the sensing system

designed to respond to a specific

and the automated pattern recognition

chemical.

system. The sensing system can be an array

of

several

different

each

element

measures

a

single

sensing

chemical sensors.

(e.g.,

Artificial

networks

analyze complex data and to recognize

can be a combination.

patterns, are showing promising results

Each chemical vapor presented to

in chemical vapor recognition. When an

the sensor array produces a signature or

ANN is combined with a sensor array,

pattern characteristic of the vapor. By

the number of detectable chemicals is

presenting many different chemicals to

generally greater than the number of

the sensor array, a database of signatures

sensors [1]. Also, less selective sensors

is built up. This database of labeled

which are generally less expensive can

signatures is used to train the pattern

be used with this approach. Once the

recognition system. The goal of this

ANN is trained for chemical vapor

training process is to configure the

recognition,

recognition system to produce unique

identification quantity

operation

consists

of

propagating the sensor data through the

classifications of each chemical so that

The

neural

(ANNs), which have been used to

measurements for each chemical, or it

beimplemented.

the

and difficult to build highly selective

spectrometer) that produces an array of

automated

approach,

being monitored. It is both expensive

different

device

this

least as great as the number of chemicals

property of the sensed chemical, or it can a

With

number of unique sensors must be at

sensing

elements (e.g.,chemical sensors), where

an

of

One approach to chemical vapor

.

be

analysis

network. Since this is simply a series of

can

vector- matrix multiplications, unknown

and

chemicals can be rapidly identified in the

complexity of the data collected by

field. Electronic noses that incorporate

sensors array can make conventional

5

DocumentToPDF trial version, to remove this mark, please register this software. ANNs

have

standard

multilayer

various applications.

network

trained

Some of these applications will be

backpropagation algorithm and the fuzzy

discussed later in the paper. Many ANN

ARTmap

configurations and training algorithms

operation a chemical vapor is blown

have been used to build electronic noses

across the array, the sensor signals are

including backpropagation-trained, feed-

digitized and fed into the computer, and

forward

the ANN (implemented in software) then

demonstrated

in

networks; fuzzy ARTmaps;

Kohonen’s (SOMs);

been

self-organizing learning

vector

maps

with

algorithm

identifies

the

feed-forward

[2].

chemical.

the

During

This

quantizers

identification time is limited only by the

(LVQs); Hamming networks; Boltzmann

response time of the chemical sensors,

machines; and Hopfield networks.

which is on the order of seconds. This

Figure 1 illustrates the basic schematic

prototype nose has been used to identify

of an electronic nose.

common household chemicals by their odor [3].

FIG:4 Schematic diagram of EN 2.1

PROTOTYPE

FIG: 5 Photograph of the prototype

ELECTRONIC

electronic nose

NOSE One of our prototype electronic

Figure 3 illustrates the structure of the

noses, shown in Figure 2, is composed

ANN. The nine tin-oxide sensors are

of an array of nine tinoxide vapor

commercially

sensors, a humidity sensor, and a

gas sensors obtained from Figaro Co.

temperature sensor coupled with an

Ltd. (Sensor 1, TGS 109; Sensors 2 and

ANN.

3, TGS 822; Sensor 4, TGS 813; Sensor

available

Taguchi-type

were

5, TGS 821; Sensor 6, TGS 824; Sensor

constructed for this prototype: the

7, TGS 825; Sensor 8, TGS 842; and

Two

types

of

ANNs

6

DocumentToPDF trial version, to remove this mark, please register this software. Sensor 9, TGS 880). Exposure of a tin-

are presented to the system. By training

oxide sensor to a vapor produces a large

on samples of various chemicals, the

change in its electrical resistance.

ANN learns to recognize the different

The humidity sensor (Sensor 10: NH-02)

chemicals. This prototype nose has been

and the temperature sensor (Sensors 11:

tested on a variety of household and

5KD-5)

office

are

used

to

monitor

the

supply

chemicals

ammonia,

including

conditions of the experiment and are also

acetone,

ethanol,

glass

fed into the ANN.

cleaner, contact cement, correction fluid, iso-propanol, lighter fluid, methanol, rubber cement and vinegar. For the results shown in the paper, five of these chemicals were used: acetone, ammonia, isopropanol, lighter fluid, and vinegar. Another category, “none” was used to denote the absence of all chemicals except those normally found in the air which resulted in six output categories from the ANN. Table 1 lists the training Parameters for one backpropagation and

FIG:6

Structure

of

one fuzzy ARTmap network.

the

Backpropagation

backpropagation ANN used in the prototype

to

Architecture: 11-11-6 feedforward

identifyhousehold

Activation: Logistic Sigmoidal

chemicals

Learning Rate: 0.10

Although each sensor is designed

Momentum: 0.90

for a specific chemical, each responds to a

wide

variety

of

No. of Epochs: 1369

chemicals.

Fuzzy ARTMap

Collectively, these sensors respond with

Training Vigilance: 0.98

unique signatures (patterns) to different

Testing Vigilance: 0.80

chemicals. During

No. of Epochs: 3 the

training

process,

ANN training parameters

various chemicals with known mixtures 7

DocumentToPDF trial version, to remove this mark, please register this software. Both

networks

were

trained

using

Fluid Vinegar

randomly selected sample sensor inputs.

66

21

The ANNs used here were not trained to

68

26

1

2

the analytes. This allowed the ANN to

Table

1:

This paper was presented at the IEEE

backpropagation

Northcon/Technical

ARTmap (FA)

quantify the concentration level of the

Amm & Vinegar Isopr & Vinegar

identified analytes, but were trained with

92.6 81.0 95.2 92.3 76.9 00.0 00.0

samples with different concentrations of

Applications

ANN

performance (BP)

and

for fuzzy

Conference (TAC’95) in Portland, OR,

Figures 4 and 5 illustrate the responses

USA on 12 October 1995. generalize

of

well on the test data set. Performance

classification for a variety of test

levels

chemicals presented to the ANNs. The

of the

two

networks were

the

sensors

and

the

ANN

basically equivalent ranging from 89.7%

ANN was able to correctly classify the

to 98.2% correct identification on the

test samples with only small residual

test set depending on the random

errors. While the ANN used here was

selection of training patterns. Table 2

not trained to quantify the concentration

summarizes

level of the identified analytes, it was

one

set

of

network

trained with samples with different

performances for novel sensor inputs.

concentrations of the analytes. This Num Train

Num Test

Input Substance

67

28

None

75

22

Acetone

64

14

Ammonia

93

28

Isopropanol

5

3

106

25

Ammonia &Isopr Lighter Fluid Amm & Lig

74

27

% Correct BP FA 96.4 96.4 100 100 100 100 92.9 100 00.0 66.7 100 96.0 100

allowed the ANN to generalize well on the test data set. From the responses of the sensors to the analytes, one can easily see that the individual sensors in the array are not selective (Figure 4). In addition, when a mixture of two or more chemicals is presented to the sensor array, the resultant

pattern (sensor

values) may be even harder to analyze (see Figure 5: c, d, and e). Thus, analyzing

8

the

sensor

responses

DocumentToPDF trial version, to remove this mark, please register this software. separately may not be adequate to yield

2.3

ELECTRONIC

the classification accuracy achieved by

MEDICINE

analyzing the data in parallel.

NOSES

FOR

Because the sense of smell is an important sense to the physician, an

2.2 ELECTRONIC NOSES FOR

electronic nose has applicability as a

ENVIRONMENTAL MONITORING

diagnostic tool. An electronic nose can

Enormous amounts of hazardous waste

examine odors from the body (e.g.,

(nuclear, chemical, and mixed wastes)

breath, wounds, body fluids, etc.) and

were generated by more than 40 years of

identify possible problems. Odors in the

weapons’

breath

production

in

the

U.S.

can

be

indicative

of

Department of Energy’s weapons’

gastrointestinal problems, sinus

complex.

Northwest

problems, infections, diabetes, and liver

National Laboratory is exploring the

problems. Infected wounds and tissues

technologies

perform

emit distinctive odors that can be

environmental restoration and waste

detected by an electronic nose. Odors

management in a cost effective manner.

coming from body fluids can indicate

This effort includes the development of

liver and bladder problems. Currently,

portable, inexpensive systems capable of

an electronic nose for examining wound

real-time identification of contaminants

infections is being tested at South

in the field. Electronic noses fit this

Manchester University Hospital.

The

Pacific

required

to

category.

A more futuristic application of

Environmental

applications

of

electronic

noses

has

been

recently

electronic noses include analysis of fuel

proposed for telesurgery. While the

mixtures [4], detection of oil leaks [5],

inclusion of visual, aural, and tactile

testing ground water for odors, and

senses

identification of household odors [3].

widespread, the sense of smell has been

Potential

include

largely ignored. An electronic nose will

identification of toxic wastes, air quality

potentially be a key component in an

monitoring, and monitoring factory

olfactory input to telepresent virtual

emissions.

reality systems including telesurgery.

applications

9

into

telepresent

systems

is

DocumentToPDF trial version, to remove this mark, please register this software. The

electronic

nose

3. CONCLUSION

would

identify odors in the remote surgical environment.

These identified

Thus

an

“Artificial

Neural

odors

Network” is developed to make the

would then be electronically transmitted

computer think like a human brain. And

to another

an electronic nose is a device intended to

site where an odor generation system

detect odors or flavors. Over the last

would recreate them.

decade, “electronic sensing” or “e-

2.4 ELECTRONIC NOSES FOR THE

sensing” technologies have undergone

FOOD INDUSTRY

important developments from a technical

Currently, the biggest market for

and commercial point of view. The

electronic noses is the food industry.

expression “electronic sensing” refers to

Applications of electronic noses in the

the capability of reproducing human

food industry include quality assessment

senses using sensor arrays and pattern

in food production , inspection of food

recognition systems. For the last 15

quality by odor, control of food cooking

years as of 2007, research has been

processes, inspection of fish, monitoring

conducted

the

checking

commonly referred to as electronic

rancidity of mayonnaise, verifying if

noses, that could detect and recognize

orange juice is natural, monitoring food

odors and flavors.

fermentation

process,

and beverage odors, grading whiskey, inspection

of

beverage

to

develop

technologies,

These devices have undergone

containers,

much development and are now used to

checking plastic wrap for containment of

fulfill industrial needs.

onion odor, and automated flavor control to name a few. In some instances electronic noses can be used to augment or replace panels of human experts. In other cases, electronic noses can be used to reduce the amount of analytical chemistry that is performed in food production especially when qualitative results will do.

10

DocumentToPDF trial version, to remove this mark, please register this software. REFERENCES [1] B.S. Hoffheins, Using Sensor Arrays and Pattern Recognition to Identify Organic Compounds. MS-Thesis,

The

University

of

Tennessee, Knoxville, TN, 1989. [2] G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, and D.B. Rosen, “Fuzzy ARTMAP: A Neural

Network

Incremental

Architecture

Supervised

Learning

for of

Analog Multidimensional Maps,” IEEE Transactions on Neural Networks, vol. 3, 698 -713. [3] P.E. Keller, R.T. Kouzes, and L.J. Kangas, “Three Neural Network Based Sensor

Systems

Monitoring,”

for

IEEE

Environmental Electro

94

Conference Proceedings, Boston, MA, 1994, pp. 377-382. [4] R.J. Lauf and B.S. Hoffheins, “Analysis of Liquid Fuels Using a Gas Sensor Array,” Fuel , vol. 70, pp. 935-940, 1991. [5] H.V. Shurmur, “The fifth sense: on the scent of the electronic nose,” IEE Review, pp. 95-58,

11